Rapid detection of replicating cells

ABSTRACT

The invention enables efficient, rapid, and sensitive enumeration of living cells by detecting microscopic colonies derived from in situ cell division using large area imaging. Microbial enumeration tests based on the invention address an important problem in clinical and industrial microbiology—the long time needed for detection in traditional tests—while retaining key advantages of the traditional methods based on microbial culture. Embodiments of the invention include non-destructive aseptic methods for detecting cellular microcolonies without labeling reagents. These methods allow for the generation of pure cultures which can be used for microbial identification and determination of antimicrobial resistance.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. application Ser. No.10/236,107, filed Sep. 6, 2002, now U.S. Pat. No. 7,582,415, whichclaims priority from U.S. Provisional Application No. 60/317,658, filedSep. 6, 2001, both of which are hereby incorporated by reference.

BACKGROUND OF THE INVENTION

The invention relates to the detection, enumeration, and identificationof replicating cells, especially microbial cells (e.g., bacteria,yeasts, and molds), in medical, industrial, and environmental samples.Microbial culture is the predominant methodology in these markets,because of its many attractive features. The invention addresses thechief drawback of microbial culture—the length of time needed to achieveresults—while retaining the beneficial attributes of the method.

Microbial Culture for Detecting and Enumerating Microbes

During the 19th and 20th centuries an understanding emerged concerningthe role of bacteria, yeast, and molds in causing infectious diseasesand determining the quality of foods and beverages. Early on, a powerfulmethod, microbial culture, was developed for detecting small numbers ofmicrobes. Microbial culture allows simple visual detection of microbesby exploiting their propensity to reproduce in large numbers rapidly.For example, a single bacterial cell, which is much too small to see byeye (about one millionth of a meter), when placed in nutrient broth, cancause the broth to become visibly cloudy in less than 24 hours.

A related microbial culture technique, called microbial enumeration orcolony counting, quantifies the number of microbial cells in a sample.The microbial enumeration method, which is based on in situ microbialreplication, generally yields one visually detectable “colony” for eachmicrobial cell in the sample. Thus, counting the visible colonies allowsmicrobiologists to determine the number of microbial cells in a sampleaccurately. To perform microbial enumeration, bacterial cells can bedispersed on the surface of nutrient agar in petri dishes (“agarplates”) and incubated under conditions that permit in situ bacterialreplication. The individual, visually undetectable, microbe replicatesrepeatedly to create a large number of identical daughter microbes atthe physical site where the progenitor microbial cell was deposited. Thedaughter cells remain co-localized (essentially contiguous) with theoriginal cell, so that the cohort of daughter cells (which may grow totens or hundreds of millions of cells) eventually form a visible colonyon the plate.

Electronic methods have been developed for enumerating microbialcolonies. Most such methods automate colony counting but do notsubstantially increase the sensitivity or decrease the time to resultscompared to traditional enumeration by eye. Colony counters use avariety of optical methods for detecting colonies including detection ofintrinsic optical properties of microcolonies (e.g., U.S. Pat. Nos.3,493,772; 3,811,036; 5,290,701; Arkin, A. P., et al. (1990);Biotechnology (N Y) 8: 746-9) and color changes of pH indicatormolecules in the matrix surrounding the colonies (U.S. Pat. No.5,510,246). Methods that use stains or probes to label the colonies havealso been developed and will be discussed below.

Microbial culture is a remarkably successful method, as evidenced by thefact that even after more than a century, the method still dominatesmedical microbiology and quality control testing in industrialmicrobiology (e.g., pharmaceutical, food, and beverage manufacturing).The method is inexpensive, relatively simple, and ultra-sensitive. Thesensitivity of microbial culture can be seen in the common test forfoodborne pathogens in ground beef. A single microscopic bacterialpathogen cell can be detected in 25 grams of ground beef using microbialculture. Another advantage of microbial culture is its ability to detecta large range of microbes of medical and industrial significance.

An advantage of in situ bacterial replication is the ability to generatea pure, or clonal, population of cells (called pure cultures, clones, orcolonies). A pure culture is a large collection of identical livingcells which descend from the same progenitor cell. Pure cultures arerequired for methods that identify microbes and for determiningantibiotic resistance. Medical microbiology relies heavily on purecultures, since bacterial pathogens are frequently isolated fromnon-sterile clinical samples (e.g., feces or wounds) along withnon-pathogenic bacteria that are likely to be even more numerous thanthe pathogenic cell. Isolating pure microbial cultures is also importantin industrial microbiology. For example, pharmaceutical and cosmeticsmanufacturers must test their products for the presence of microbialcontaminants. Pure cultures of the contaminating microbes are used formicrobial identification, which determines whether a production batchmust be discarded and aids in investigating the source of thecontamination in the industrial process.

TABLE 1 Microbial enumeration using microbial culture Advantagesultra-sensitive quantitative generates pure cultures can detect andenumerate many types of microbes in a single test can selectively growmicrobes only detects replicating cells inexpensive simple and easy toperform Disadvantages slow manual procedures and analysis not allmicrobes are culturable

The ability to culture microbes selectively is an essential tool formicrobial identification and for determining resistance andsusceptibility to antimicrobial agents such as antibiotics. Selectiveculture exploits the fact that different microbes require differentgrowth conditions. These differences arise from the fact that strains ofmicrobes differ in their biochemical makeup because of inherent geneticdifferences. For example, one type of microbe might be able to grow onnutrient medium containing the sugar sorbitol as the sole source ofcarbon atoms to fuel its growth, while another type of microbe cannot.Selective growth is important in the food industry. For example, a foodsample can be scanned for a particular food pathogen, Salmonella, byplating the sample on media that allows Salmonella to grow but not otherfood microbes.

Similarly, selective culture is used to determine which antibiotic ismost effective for killing a bacterial strain isolated from the spinalfluid of a child with bacterial meningitis. A pure bacterial culture(derived from a clonal colony from a nutrient agar plate) is used toinoculate growth medium containing various antibiotics at variousconcentrations. The optimal antibiotic therapy is determined bymonitoring the ability of the microbe to grow in the presence of thevarious antibiotics. Determining antibiotic resistance andsusceptibility by selective growth on the surface of solid nutrient agarmedium is another common approach. For example, in the Kirby-Bauermethod, small filter disks impregnated with different antibiotics areplaced on the surface of nutrient agar plates coated with a pure cultureof bacteria from a clinical sample. A gradient of antibiotic diffusesradially outward from the filter. Bacteria that are resistant to highlevels of the antibiotic grow up to the edge of the filter. However,bacteria that are very sensitive to the antibiotic can not grow unlessthey are far from the edge of the filter. After incubating the plates(usually for one or two days) a microbiologist determines the level ofresistance to an antibiotic by measuring the thickness of thegrowth-free ring or zone around the filter. A related method, the “E”test (Hardy diagnostics), uses a rectangular strip that is impregnatedwith a gradient of antibiotic. The level of bacterial resistance isdetermined by measuring the point on the strip with the highestantibiotic concentration next to which the bacteria continue toreplicate.

The most serious drawback of microbial culture is that it is slow—ittakes time to generate the number of cells required for visualdetection. The long growth period required for microbial culture is asignificant problem in both healthcare and industry. For example,because it requires days to culture and identify the microbe causing apatient's blood infection, a patient with a fungal blood infection coulddie before anti-fungal therapy is even begun. Some infectious agents,such as the bacterium that causes tuberculosis, generally require weeksto grow in culture. The long time required for detecting M. tuberculosiscan result in a patient with tuberculosis infecting many others with thehighly contagious disease or the costly quarantine of patients who donot have tuberculosis.

In food manufacture, long testing cycles can increase food spoilage orresult in moving inadequately tested material through subsequentprocessing steps. Slow microbial culture also adversely impacts theproduction of biopharmaceuticals and vaccines. In these applications,the manufacturing process often requires pooling of batches. Because oflong microbial culture testing cycles and the need to move materialthrough the manufacturing process, contaminated batches are sometimesnot detected until after a batch pooling step. If it is subsequentlyfound that a contaminated batch was combined with uncontaminatedbatches, the whole pool of combined batches must be discarded.

Other disadvantages of microbial culture, such as tedious manualprocedures and inability to culture some microbes, are considered lessproblematic than the long time required. For example, manual methods formicrobial enumeration predominate, even though instruments for automatedplating and analysis have been introduced. Most types of microbes foundin the environment cannot be grown in the laboratory. However, suchmicrobes are often not harmful to humans or are destroyed in industrialmanufacturing processes and are therefore ignored for most applications.However, several important exceptions of critical medical importanceinclude hard or impossible to culture bacteria such as Chlamydia,strains of which can cause sexually transmitted disease and pneumonia.Fortunately, alternative culture-independent methods are available inthese cases (see below).

Rapid Microbial Culture Enumeration Methods

A number of microbial culture methods for more rapid microbialenumeration have been developed. One rapid microbial culture methoddeposits bacterial cells on microscope slides coated with nutrientmedium. Using microscopic examination, microbial growth can be detectedmuch earlier than with the naked eye, since microscopes can detectmicrocolonies resulting from a small number of cell divisions. However,this method is not effective for testing large samples containing lownumbers of microbial cells, because only a very small volume of samplecan be observed in a microscopic field of view. The low sensitivity ofmicroscopic methods generally limits their usefulness to samplescontaining more than ten thousand bacterial cells per milliliter—thesemethods are much less sensitive than traditional microbial culture.

The advent of electronic imaging systems has led to the development ofnumerous automatic “colony counters.” Although, most of these countersare designed to aid the user by automating the colony counting processand do not decrease the time to result, some systems have demonstratedthe ability to detect colonies before they are large enough to be seeneasily by eye. For example, the Colifast Rapid Microcolony Counter(Colifast) can detect small fluorescently labeled colonies of coliformbacteria hours before they can be seen by eye. The Colifast systemachieves enhanced detection by using a fluorogenic compound (a substancethat is not fluorescent until metabolized by coliform bacteria) includedin the nutrient agar media.

A system for rapid enumeration of microbial colonies usingbioluminescent labeling has recently been commercialized. The MicroStarsystem (Millipore) uses the cellular ATP in microcolonies to generatelight via the action of applied luciferase enzyme and substrates. Themethod reduces time to detection substantially. The MicroStar imagingsystem has also been used in conjunction with labeled probes to identifyspecific bacteria (Stender, H., et al. J Microbiol Methods 46: 69-75(2001)). A drawback of the system is that the detection method kills themicrobes, precluding isolation of pure cultures from the colonies. Thesystem also requires an expensive image intensifier module.

An instant film-based method for detecting microcolonies containingspecific bacteria has been developed by Boston Probes (Perry-O-Keefe,H., et al. Journal of Applied Microbiology 90: 180-9 (2001)). Microbialmicrocolonies on membranes are labeled using microbe-specific PNA probestagged with an enzyme capable of generating a chemiluminescent signal.The membranes are then placed on X-ray or instant-film for imaging. Themethod is limited to scanning for a particular microbe in oneexperiment. A similar method uses fluorescently labeled PNA probes andan array scanner (Stender, H., et al. Journal of Microbiological Methods45: 31-9 (2001)). These approaches require substantially more expertisethan traditional culture methods.

Rapid Microbial Enumeration without Microbial Culture

The fastest methods for microbial enumeration forgo microbial culture.Medical and industrial microbiologists are generally interested only inenumerating viable microbes—only living microbes are capable ofreplicating during microbial culture. Therefore, to be most effective,methods that detect individual cells without reliance on cellularreplication must distinguish living from dead microbes by usingphysiological surrogates for cellular replication (e.g., Nebe-von-Caron,G., et al., J Microbiol Methods 42: 97-114., 2000; Mignon-Godefroy, K.,et al., Cytometry 27: 336-44, 1997). Cells are stained with dyes thatmeasure a biochemical property that is generally correlated with theability to replicate (e.g., esterase activity or biochemicalrespiration). Validating and instituting surrogate methods have beenproblematic since samples that are known to meet regulatory standardsand that are scored as sterile using traditional plate culturing methodsoften have thousands of cells that score positive for the surrogatebiochemical activity.

An example of a system that directly detects viable cells is the ScanRDIsystem (Chemunex). ScanRDI enumerates microbial cells that are stainedwith a fluorogenic esterase substrate using laser scanning technology(U.S. Pat. No. 5,663,057; Mignon-Godefroy, K., et al., Cytometry 27:336-44, 1997). A laser-scanning system (including an optical collectionsystem using photomultiplier tubes (PMTs)) captures an image of thefilter and can detect individual labeled cells. The system illuminatesand queries a microscopic area (generally 4-14 μm) but scans the beamprogressively so as to cover a macroscopic area (e.g., a 25 mm diametercircle). The system is designed to detect cells with intact membranesand active esterase enzyme. There is a correlation between the numbersof such cells and the number of cells that can form colonies on growthmedium. However, this approach often results in substantial“overcounting”—i.e., higher numbers of cells than are detected bytraditional culture (Costanzo, S., et al. (2002). PDA Journal ofPharmaceutical Science and Technology 56: 206-219). Another disadvantageof the ScanRDI system is that it kills the microbes during the stainingprocess precluding generation of pure cultures from the detectedmicrobes. Finally, laser scanning systems for cellular enumeration arecomplex and expensive (hundreds of thousands of dollars) making themdifficult to justify for routine microbiological applications. Otherlaser scanning systems have also been commercialized (Miraglia, S., etal., J Biomol Screen 4: 193-204, 1999; Tibbe, A. G., et al., NatBiotechnol 17: 1210-3, 1999; Kamentsky, L., 2001, Laser ScanningCytometry.

In Cytometry, Z. Darzynkiewicz, H. Crissman and J. Robinsnon, eds.Methods in Cell Biology Vol. 63, Part A, 3rd ed, Series Eds. L. Wilsonand P. Matsudaira. (San Diego: Academic Press)).

Flow cytometry is another powerful method that can rapidly enumeratemicrobes without relying on cellular replication (Alvarez-Barrientos,A., et al., Clin Microbiol Rev 13: 167-195, 2000). Individual organismsor particles are forced to flow through a narrow channel, one at a time,past a laser beam. Besides enumeration, information about size/shape andcomposition is gathered by analyzing the fluorescence emission and lightscattering caused by the organisms. Thousands of individual cells orparticles can be analyzed per minute. Pathogens can by identified usingflow cytometry by binding fluorescently labeled species-specificantibodies or nucleic acid probes to fixed organisms(Alvarez-Barrientos, 2000, supra).

Pathogens can by identified using flow cytometry by bindingfluorescently labeled species-specific antibodies or nucleic acid probesto fixed organisms (Alvarez-Barrientos, 2000, supra). Individual cellsof one particular type are usually the targets. Flow cytometric methodshave been used more extensively for quantitatively detecting particularcell types on the basis of the ability to bind labeled probes, usuallyeither antibodies or nucleic acids. For example, flow cytometry is usedto quantify the population sizes of classes of lymphocytes in patientswith AIDS. Flow cytometry is a more complex and expensive method thantraditional culture. Although faster than traditional culture, flowcytometry does not have a comparable limit of detection to thetraditional method. Traditional microbial culture can detect onebacterial cell in 0.1 liter of water, while flow cytometry is mosteffective when there at levels that are many thousands of times higherthan that. Furthermore, microbial targets are often killed by thestaining methods used for detection, eliminating the ability to producepure cultures.

Using microscopic imaging to visualize and enumerate microorganismsdirectly can be rapid and relatively simple to perform (Amann, R. I., etal., Microbiological Reviews 59: 143-69, 1995). Direct fluorescentassays (DFA) in which a fluorescently labeled antibody reacts with afixed sample is a common method in clinical diagnostics laboratories.For example, specimens suspected of containing bacterial agents areroutinely stained with Gram stain. Similarly, to test for M.tuberculosis, samples are subjected to acid fast staining. The drawbackof this technique is that it is many thousands of times less sensitivethan microbial culture. The low sensitivity is due to the small fieldsvisualized at high magnification. Only at high target cellconcentrations are small fields likely to contain a target cell. Thus,for example, reliable identification of bacterial pathogens in sputumusing fluorescent in situ hybridization requires titers of about 4×10⁵cells/ml or more. Clinical samples obtained in common medicallysignificant infections may contain fewer than 100 cells/ml—aconcentration that is not nearly high enough to expect to find a cell ina high power microscopic field.

A system that does have the sensitivity to detect single bacterial cellsusing large area non-magnified imaging has been developed by researchersat Hamamatsu Corporation (Masuko, M., et al., FEMS Microbiol Lett 67:231-8, 1991; Masuko, M., et al., FEMS Microbiol Lett 65: 287-90, 1991;Yasui, T., et al., Appl Environ Microbiol 63: 4528-33, 1997). Large areaimaging of individual microscopic target cells is accomplished using anultrasensitive photon-counting CCD camera coupled to a fiber opticsystem, image intensifier, and Image-Processor. A disadvantage of thissystem is the great expense incurred because of the incorporation of theimage intensifier and associated optics. Furthermore, unlike microbialculture methods, the system can not detect any microbe, distinguishbetween living and dead microbes, or generate pure cultures.

Rapid Microbial Enumeration by Quantifying Molecular Constituents ofCells

Numerous methods for detecting and identifying microbes based on theirmolecular constituents have been developed in the last half-century.Although some of these methods are substantially faster than microbialculture, none offers all of the features of culture that are critical tomicrobiologists. For example, although numerous immunoassays formicrobes have been commercialized, this technique is not inherentlyquantitative, is much less sensitive than microbial culture, and is notas powerful as culture for detecting many types of microbes in a singletest. Or, as another example, nucleic acid amplification methods can beas sensitive as microbial culture, but they do not distinguish betweenliving and non-living cells and can not deliver pure cultures forantibiotic susceptibility testing. Methods for biochemical analysis(e.g., of fatty acids, nucleic acids, or proteins) usingelectrophoresis, mass spectroscopy, and chromatography can be powerfulfor microbial identification, but such methods are usually inappropriatefor microbial enumeration and are generally too expensive and complexfor routine microbial diagnostics.

Unmet Needs for Microbial Enumeration

In summary, current microbial enumeration testing is dominated bymicrobial culture. Microbial culture has the important advantages ofbeing simple, ultra-sensitive, inexpensive, and quantitative but has thesignificant drawback of being slow. The long time required for resultshas major costs in healthcare and in manufacturing. More rapid methodshave been developed, but while improving the time to results, they havesacrificed one or more of the critical advantages of microbial culture.

Thus, there is need for a test that is faster than traditional microbialculture but that retains the key benefits of the traditional method.

SUMMARY OF THE INVENTION

The invention enable efficient, rapid, and sensitive enumeration ofliving cells by detecting microscopic colonies derived from in situ celldivision using large area imaging. Microbial enumeration tests based onthe invention address an important problem in clinical and industrialmicrobiology—the long time needed for detection of traditionaltests—while retaining key advantages of the traditional methods based onmicrobial culture. Embodiments of the invention include non-destructiveaseptic methods for detecting cellular microcolonies without labelingreagents. These methods allow for the generation of pure cultures whichcan be used for microbial identification and determination ofantimicrobial resistance.

The invention features a method for detecting living target cells in asample including the steps of providing living target cells present inthe sample in a detection zone including a detection area at a densityof less than 100 target cells per mm² of the detection area, allowingthe formation of one or more microcolonies of the target cells by insitu replication; and detecting one or more microcolonies, wherein thereplication produces one or more microcolonies; wherein the longestlinear dimension of the detection area is greater than 1 mm; within thedetection area, the cells are randomly dispersed and immobilized; thedetecting detects one or more microcolonies that have a mean measurementof less than 50 microns in at least two orthogonal dimensions; and thecells in the one or more microcolonies remain competent to replicatefollowing the detection step.

The invention further features a method for detecting microcolonies oftarget cells including the steps of providing target cells in adetection zone, wherein within the detection area, the cells arerandomly dispersed and immobilized; allowing the formation of one ormore microcolonies of the target cells by in situ replication, whereinat least one of the microcolonies includes fewer than 100 target cells;and detecting one or more naturally occurring optical properties of theone or more microcolonies using less than 5 fold magnification.

The invention also features an instrument for detecting microcolonies oftarget cells that includes a photoelectric array detector having anoptical resolution of less than 20 microns and encircled energy valuesof greater than 70% per pixel; and an illumination source, wherein theinstrument is capable of illuminating and simultaneously imaging adetection area having at least one dimension that is ≧1 cm, and whereinthe instrument does not optically magnify more than 5 fold.

ADVANTAGES OF THE INVENTION

Some advantages of various embodiments of the invention are listed inTable 2.

TABLE 2 Embodiment Advantages Reagent-less fluorescent Minimal changesto accepted practices detection and enumeration Faster and lower riskregulatory path of microcolonies Low cost of goods System simplicityEnables non-destructive testing (below) Collection optics optimizedShort time-to-detection for detecting living microcolonies Non-magnifiedlarge area Allows ultra-sensitive detection imaging of individual liveAllows large dynamic range microcolonies on membranes Allows broad rangeof sample volumes High signal:background ratio at low titersNon-destructive enumeration Allows generation of pure cultures (i.e.,microbes are not killed) Allows microbial identification Allowsdetection of antimicrobial resistance Allows internal validation (below)Internal comparison with Streamlines demonstration of traditionalvisible colonies equivalence to validate methods Imaging livemicrocolonies in Allows multiple reads sterile (closed) disposableMinimizes false positives Methods & software uniquely Added detectionrobustness, specificity discriminate growing microbes Allows detectionin complex samples from artifacts

The invention's short time needed to achieve results derives from theinvention's ability to detect microcolonies containing only a smallfraction of the cells that are required by the traditional methods.Since cell replication requires time, detecting small microcoloniesusing the invention provides results faster than detecting the largevisible colonies using traditional enumeration methods. To detect smallmicrocolonies the invention uses a combination of efficient signalgeneration and signal detection methods.

The ultra-sensitivity—its ability to detect small numbers of microscopiccells in large samples—stems, in part, from the use of large areaimaging. For example, the invention can detect microscopic colonieswithout magnification. This feature allows a large area to be surveyedfor microcolonies in a single image. Imaging a large area is a key tothe invention's ability to efficiently analyze large sample volumes. Forexample, the microbial contaminants in a large volume of a sample can bedeposited on a membrane using membrane filtration. The invention usinglarge area non-magnified imaging of microcolonies can analyze the entiremembrane efficiently. In contrast, using a high magnification microscopeto evaluate the microcolonies on the same filter might require thousandsof images.

The power to enumerate small numbers of microcolonies in a large areaefficiently also comes from the invention's ability to use imagingapproaches that compare object signals to local backgrounds. Thisability improves the signal to background ratio for samples containingfew cells over methods that integrate the total signal and background ina large area.

Assay robustness for samples with few cells is provided by theinvention's inherent ability to enumerate growing microcolonies. Thus,the invention can decrease false positives over methods which detect asingle integrated signal, such as methods that quantify the presence ofbiomolecules (e.g., ATP, antigens, or nucleic acids). Any artifact thatcauses a signal can generate a false positive when using methods thatrely solely on integrated signal. Consider a sample that contains 482microbial cells each of which generate 100 fluorescent units. The resultof an integrative method is a single number (48,200 fluorescent units).Artifacts that generate a similar number of fluorescent units, forexample, a large fluorescent dust particle may be indistinguishable. Theinvention, however, can easily distinguish between a single large dustfluorescent dust particle and 482 individual growing microcolonies.

Detecting growing microcolonies is a powerful method for discriminatingagainst false positive signals from inanimate objects and cellsincapable of growth under the test conditions. For example, consider atest to detect microbial microcolonies on a membrane lying on solidgrowth media in a petri dish. In one embodiment of the invention, thedetection area is imaged before allowing the microbes in the detectionarea to grow into microcolonies. If some fluorescent dust particles orautofluorescent mammalian cells are present in the detection area somepositive signals will be apparent in this “zero time” image. Afterincubating the petri dish to allow for microbial replication anotherimage is taken. When the two images are aligned in register, thepositive signals that correspond to microcolonies can be distinguishedfrom the false positives since the false positives are present (usuallyunchanged) in the “zero time” image and the post-incubation image. Onlygrowing microcolonies should appear over time. To confirm themicrocolony signals, images can be acquired and compared at multipletime points during the incubation. Only growing microcolonies shouldincrease in signal strength and in size over time.

Tests constructed using the invention can have a large dynamic rangecompared to tests constructed using methods in the prior art. Thus, forexample, a test based on the invention designed can detect from one to10⁶ microcolonies in a single image. In contrast, traditional microbialenumeration methods work best when about 30 to 150 colonies aredeposited on a filter (47 mm diameter). New enumeration methods (e.g.,Chemunex's ScanRDI and Millipore's MicroStar) also have limited dynamicranges.

To achieve efficient signal generation, the invention can exploit eitherthe intrinsic optical properties of the microcolonies (e.g.,autofluorescence, reflectance, or light scattering) or variousexternally applied labeling reagents. The ability to exploit a range ofoptical properties and labeling methods enables creation of importantmicrobiological tests. For example, using a method that detects aubiquitous property of microcolonies (e.g., autofluorescence or infraredabsorption) is useful for tests that enumerate total microbial contentof a sample. Such tests are critical in food processing for determiningthe likelihood of spoilage and for finished product release testing inpharmaceutical manufacture. One important embodiment of the inventionuses a reagent-less system based on detecting cellular autofluorescenceto detect small microbial microcolonies. This embodiment provides asimple, non-destructive, aseptic approach to microbial enumeration. Todetect specific types of cells, category-specific labeling reagents canbe used. For example, a fluorescently labeled antibody that specificallybinds to Listeria monocytogenes can be used to detect microcoloniesderived from cells of this important food pathogen.

Like traditional microbial culture, the invention can exploit thediagnostic power of measuring microbial growth under selectiveconditions. For example, to determine bacterial resistance toantibiotics bacteria can be grown on growth medium onto which antibioticdisks have been placed. The size of the no-growth zone near the disksdetermines antibiotic resistance. The invention can be used to detectthe size of this zone more rapidly. Similarly, the invention can be usedto detect the growth of specific microbes on selective medium rapidly.

Simplifying the obligatory test validation cycle in which a new methodis shown to be equivalent to the “gold standard” method is anotheradvantage of the invention that derives from non-destructiveenumeration. The invention facilitates equivalence to the “goldstandard” culture tests by allowing an internal comparison of the newand old methods. Briefly, after imaging the microcolonies derived frommicrobes in a sample at an early time point, the samples can bere-incubated for the amount of time required when using traditionalvisual detection of colonies. In this way an internal comparison can bemade between the invention's enumeration of the microcolonies and theenumeration of the same colonies at a later time by the traditionalmethod.

Other features and advantages will be apparent from the followingdescription and the claims.

By target cell is meant a cell that is potentially present in a sampleand whose presence is assayed by the invention.

By category of target cells is meant multiple target cells that areconsidered identical for the purposes of a test constructed using theinvention.

Consider a test designed to detect any strain of E. coli bacteria. Forthe purposes of the test, the category “E. coli” would thus include anybacterium in the species E. coli. Such a test would be designed todetect, without differentiation, any bacterium in the species E. coli.Bacteria, and other target cells, that are not E. coli would either notbe detected in this test, or would be detected and identified as notbeing members of the group E. coli. In contrast, consider a testdesigned to detect the pathogen E. coli O157:H7, a subgroup of the E.coli species. In this case, the subgroup E. coli O157:H7 is a categoryof target cells. Bacteria in the subgroup, i.e., in the category “E.coli O157:H7”, are detected without differentiation. E. coli that arenot in the E. coli O157:H7 subgroup are not detected by the test and aretherefore not in the E. coli O157:H7 category.

Categories need not be taxonomically related as in the previousparagraph. For example, a test might be designed to detect the categoryof bacteria that makes a protein that is required to confer resistanceto the antibiotic vancomycin. This protein could be made by bacterialstrains that are not closely related, i.e., that are members ofdisparate species. A vancomycin resistant strain in one species,however, is likely to be very closely related to vancomycin sensitivestrains in the same species. The category of bacteria that make the vanAprotein (important for achieving vancomycin resistance), for instance,includes vancomycin-resistant bacteria in the genus Enterococcus and inthe genus Staphylococcus, while the majority of enterococci andstaphylococci are not included in the category. Thus, in this case, itcan be seen that the category encompasses target cells that areconsidered, for the purposes of the test, to be identical because of acommon feature, in this case a molecular component (a category-specificbinding site) rather than to a common phylogenetic (genealogical)relationship.

By non-overlapping categories of target cells is meant sets of targetcells whose union is the null set. That is, the category of all E. colibacteria, the category of all bacteria in the genus Pseudomonas, and thecategory of all fungi are non-overlapping categories. That is, no memberof any of the categories is a member of any of the other sets.

By the categorical complexity of a test is meant the number ofnon-overlapping categories that are detected in the test.

By a category-specific binding site is meant a site on a target cellthat specifically binds to a category-binding molecule underspecific-binding conditions and that distinguishes target cells that aremembers of a particular category to be identified in a test from targetcells that are not members of that category but might also be present inthe test sample. That is, the site is present typically on all membersof one category, and typically not on any members of non-overlappingcategories. Category-specific binding sites specifically bind tocategory-specific binding molecules.

If a test scans a sample for a category of target cells that constitutesa taxonomic group, a category-specific binding site is one that ispresent in essentially all members of that taxonomic group, but is notpresent in essentially all members of other taxonomic groups that mightbe present in the test sample.

Alternatively, a test might scan a sample for category-specific bindingsites that are shared by members of different taxonomic groups. Examplesof this type of category-specific binding site include variousmacromolecules (e.g., DNA) and genes, mRNAs, and proteins that conferantibiotic resistance, confer virulence, or indicate viability. Acategory-specific binding site is often a part of a larger molecule orcomplex. For example, a category-specific genomic sequence can be usedas a category-specific binding site in a test. Such a category-specificbinding site is part of a much larger genome that contains (1) sectionsthat are not category-specific; (2) sections that are category-specificbinding sites but for which the test does not scan; and (3) othersections that are distinct category-specific sequences for which thetest does scan.

Binding sites that are present, e.g., in 80%, 90%, 95%, or more than 99%of the target cells that are members of a category but that are absent,e.g., in 80%, 90%, 95%, or more than 99% of the target cells that aremembers of all other categories of the same class, are consideredcategory-specific binding sites. Note that a category-specific bindingsite can be trivially or exceptionally absent from a target cell that isa member of the category. Similarly, a category-specific binding sitecan be trivially or exceptionally present in a target cell that is not amember of a category. For example, consider a protein site that occursin essentially all E. coli bacteria but in no other bacterial species.If, as might be the case in less than one cell out of millions ofbacteria, a mutation causes the protein not to be produced, the markerwill not be present in that strain of E. coli. However, this proteinsite is still considered a category-specific binding site.Alternatively, the gene for the same protein is transferred to a strainof a different species of bacteria by recombinant DNA technology or bynatural means (e.g., by viral transduction). In this case, a bacterialstrain that is not a member of the category E. coli would express whatwould still be considered an E. coli-specific binding site.

By category-binding molecule is meant a molecule or molecular complexthat specifically binds to a category-specific binding site. Examples ofcategory-binding molecules are nucleic acid probes that hybridize togenomic DNA; nucleic acid aptamers that have been selected or “evolved”in vitro to bind specifically to sites on proteins; antibodies that bindto cellular antigens or serum proteins; and ligands such as epidermalgrowth factor or biotin that bind specifically to hormone receptors orto binding molecules, such as avidin. Two category-binding molecules aresaid to be distinct if they bind to distinct and non-overlappingcategory-specific binding sites. Category-binding molecules may bereferred to according to their molecular composition, e.g., a categorybinding oligonucleotide, probe, antibody, ligand, etc.

By a category-binding molecule that specifically binds to a category oftarget cells is meant a category-binding molecule that binds underdefined binding conditions to essentially all target cells that aremembers of a category scanned for by a test, but to essentially notarget cells that are not members of the category but that are likely tobe present in the sample. The number of category-binding molecules thatare bound by target cells in a category scanned for as compared to thenumber bound by target cells not in such a category, are typicallytwo-fold, five-fold, ten-fold, or greater than fifty-fold greater.

By binding conditions is meant the conditions used in a test to achievespecific binding of category-binding molecules to category-specificbinding sites. For example, when the category-binding molecules arecategory-specific DNA probes, the binding conditions for a particulartest might be stringent DNA hybridization conditions. The appropriatestringent DNA hybridization conditions depend on the nature of theprobes, as is well known by those familiar with the art. For example,for typical DNA probes of length greater than 500 bases, an appropriatebinding condition (usually referred to as a “washing condition” in thehybridization vernacular) is 65° C. at 0.2×SSC. For binding an antibodyto an antigen, typical binding conditions are room temperature inPBS-TB.

By a family of category-binding molecules is meant a set ofcategory-binding molecules that specifically bind to a particularcategory of target cells.

Polyclonal antibodies generally constitute families of category-bindingmolecules since they generally comprise multiple distinctcategory-binding molecules that bind to the same category of targetcell. Note that, unless affinity purification is used, polyclonalantibody preparations typically also contain antibodies that do not bindto the chosen category of target cell and may contain antibodies thatbind to other categories. Additional antibodies are present because theantibody repertoire of an animal is determined by the animal's infectionhistory. Therefore, polyclonal antibodies are preferably purified byaffinity methods. Category-binding molecules in a family might bind tosome target cells in the category but not to others.

Another example of a family of category-binding molecules is a set of 80category-specific genomic DNA sequences that occur in all E. coliO157:H7 strains but that do not occur in members of other groups ofbacteria. This family of category-binding molecules can hybridize as agroup to suitably prepared E. coli O157:H7 cells, but does not hybridizeto other categories of cells. Families can include different types ofcategory-binding molecules. For example, a monoclonal antibody thatspecifically binds to the O157 antigen and one that binds to the intiminprotein (a virulence factor) could also be included in the above familyof category-binding molecules. A family of category-binding moleculescan comprise any number of category-binding molecules (i.e., one ormore).

By non-overlapping families of category-binding molecules is meantfamilies of category-binding molecules in which each family bindsspecifically to one, and only one, category in a set of non-overlappingcategories. That is, a set of non-overlapping families ofcategory-binding molecules map to a congruent set of non-overlappingcategories. For example, in a test that scans the 4 USP objectionableorganisms E. coli, Salmonella, Pseudomonas spp., and Staphylococcusaureus, there are four non-overlapping categories. Such a test mightincorporate four different non-cross-reacting polyclonal antibodies,each specific for one of the test categories. Thus, the test comprisesfour non-overlapping families of category-binding molecules. Thenon-overlapping families of category-binding molecules in a test arecalled an ensemble of category-binding molecules.

By an ensemble of category-binding molecules is meant a set of one ormore non-overlapping families of category-binding molecules that arecombined in a mixture for a particular test. Tests that scan formultiple non-overlapping categories of target cells comprise one familyof category-binding molecules per category. The entire set ofcategory-binding molecules, that comprise these families, is referred toas an ensemble.

By the category-binding molecule complexity of an ensemble is meant thenumber of distinct category-binding molecules or moieties in anensemble. For example, if an ensemble of category-binding moleculesconsisted of 234 oligonucleotide probes, the category-binding moleculecomplexity of the ensemble would be 234.

By the family complexity of an ensemble is meant the number ofnon-overlapping families of category-binding molecules in an ensemble.The family complexity is the same as the minimum number of target cellsrequired to bind a category-binding molecule from each of the familiesin an ensemble. The family complexity of a test corresponds to thecategorical complexity of a test—i.e., the number of distinct categoriesfor which the sample is scanned. In general, the family complexity alsocorresponds to the number of distinct signal signatures used in a test.

By signal element is meant a molecule or particle that directlygenerates a detectable signal. The phrase “directly generates” refers tothe fact that signal elements are the immediate source or criticalmodulator of the detectable signal. Thus, if the signal is photons thatarise from a fluorophore, the fluorophore is the immediate source of thephotons and, therefore, is a signal element. If the signal is photonsscattered by an RLS particle, the RLS particle is a signal element.Alternatively, if the signal is the light transmitted or scattered froma chromogenic precipitated product of the enzyme horseradish peroxidase,the chromogenic product is the signal element.

A characteristic of a signal element is that such an element cannot bedivided into parts such that each part generates a signal that iscomparable (in character, not necessarily in intensity) to the whole.Thus, a 2 nM diameter quantum dot is a signal element, as dividing itchanges the character (emission spectrum) of the resulting nanocrystals.A 5 μm particle impregnated with a fluorescent dye such as fluorescein,is not a signaling element, since it could be divided into parts suchthat each part has signaling characteristics comparable to the intactparticle. The molecule fluorescein, in contrast, is a signaling element.The detectable products of signal generating enzymes (e.g., luciferase,alkaline phosphatase, horseradish peroxidase) are also considered signalelements. Such signal elements (or their precursors when there is achemical conversion of a precursor to a signal element) may bediffusible substances, insoluble products, and/or unstableintermediates. For example, the enzyme alkaline phosphatase converts thechemiluminescent substrate CDP-Star (NEN; catalog number NEL-601) to anactivated product, which is a photon-emitting signal element.

By signaling moiety is meant a molecule, particle, or substancecomprising or producing (in the case of enzymes) one or more signalelements and that is or can be conjugated to a category-bindingmolecule. The signaling moiety can be attached to the category-bindingmolecule either covalently or non-covalently and either directly orindirectly (e.g., via one or more adaptor or “chemical linker”moieties). Examples of signaling moieties include carboxylated quantumdots; a fluorophore such as Texas Red that is modified for binding to anucleic acid probe or an antibody probe; streptavidin-coated fluorescentpolystyrene particles (which can be conjugated to biotinylatedcategory-specific binding proteins); a rolling-circle replicationproduct containing repeated nucleic acid sequences each of which canhybridized to several oligonucleotides tailed with fluorescentlymodified nucleotides and which contains a category-specific bindingoligonucleotide at the 5′ end. A signaling moiety can comprisephysically distinct elements. For example, in some cases the signalingmoiety is an enzyme (e.g., alkaline phosphatase) that is conjugated to acategory-binding molecule (an antibody, for example). Signal isgenerated when a substrate of alkaline phosphatase (e.g., CDP-Star, orBM purple from NEN and Roche, respectively) is converted to productsthat are signal elements (e.g., an unstable intermediate that emits aphoton, or a precipitable chromogenic product). It is not unusual forthe category-binding molecules, enzymatic signaling moieties, andsubstrate to be applied to the reaction at distinct times.

By signaling moiety complex is meant a physical cell that comprises morethan one signaling moiety and more than one category-binding molecule.The physical association of the signaling moieties and category-bindingmolecules in a signaling moiety complex must be stable (e.g., thesignaling moieties and category-binding molecules should have meanhalf-lives of association with the complex of at least one day in PBS at4° C.). As an example of a signaling moiety complex, consider apolystyrene microparticle that is coated with thousands of molecules oftwo types: a target cell-specific antibody and alkaline phosphatase.Such a signaling moiety complex binds to the target cell via theconjugated antibody category-binding molecule. When incubated with achromogenic alkaline phosphatase substrate (the signal element; e.g., BMpurple, Roche), a colored spot can be generated that can be detected byeye. Alternatively, the same signaling moiety complex, when incubatedwith either a chemiluminescent or a fluorescent alkaline phosphatasesubstrate, generates either a chemiluminescent or fluorescent signal.Further examples of signaling moiety complexes include: nanogoldparticles conjugated to fluorescein-labeled antibodies, and latexparticles conjugated to both oligonucleotide category-binding moleculesand acridinium esters that chemiluminesce upon addition of hydrogenperoxide.

By signal character of a signal element or signal moiety is meant theaspect or aspects of a signal generated by the signal element signalingmoiety that is useful for distinguishing it from other signal elementsor signaling moieties. For example, the signal character of a signalingmoiety labeled with fluorescein and rhodamine is fluorescence. Thecharacter of a radio transponder is radio frequency. Examples ofphotonic signaling character are fluorescence, light scattering,phosphorescence, reflectance, absorbance, chemiluminescence, andbioluminescence. All but the latter two examples of photonic signalingcharacter depend on external illumination (e.g., a white light source, alaser light source, or daylight). In contrast, chemiluminescence andbioluminescence are signaling characters that are independent ofexternal light sources.

By the class of a signal element or signaling moiety is meant thedistinct quality of the signal that is useful for distinguishing it fromother signal elements or signaling moieties. For example, a liposomethat is labeled with red dye is distinguished from differently coloredliposomes. The color red is its class. For a micro-transmitter thatbroadcasts a particular radio-frequency signal, the quality of theradio-frequency signal that differentiates the micro-transmitter fromother micro-transmitters constitutes the signal element class.

By signal signature is meant the distinctive signaling quality of thecombination of signaling moieties that bind to a category of targetcells in a test. A target cell that is bound to four types ofantibodies, one of which is conjugated to a fluorescein molecule, andthree of which are conjugated with rhodamine molecules has a signalsignature that is described by the combined weighted absorbance andemission spectra of fluorescein and rhodamine.

By signal complexity of a test or an ensemble of labeledcategory-binding molecules is meant the number of categories of targetcells that can be distinctly labeled in the test or by binding to theensemble. Alternatively, the signal complexity is defined as the numberof distinct signal signatures that would be expected to occur if amember of each category of target cell were present. For some tests, thesignal complexity of an ensemble of category-binding molecules is thesame as the number of categories for which the test scans. Other tests,which scan for many categories, may only have a signal complexity ofone.

By selection force is meant a force that is used to capture, isolate,move, or sequester target cells. Examples of selection forces includegravity, magnetism, electrical potential, centrifugal force, centripetalforce, buoyant density, and pressure. Target cells can be mobilized by aselection force acting on the target cell alone. Alternatively,selection forces can act specifically on target cells that areassociated with selection moieties (see definition below).

Examples of the application of selection forces to mobilize target cellsinclude: centrifugation of target cells; magnetic selection of targetcells bound to magnetic particles; gravitational sedimentation of targetcells labeled with metallic particles; and deposition of target cells ona porous membrane by vacuum filtration.

By selection moiety is meant an atom, molecule, particle, or cell thatcan be conjugated to a category-binding molecule and that confers on thecategory-binding molecule the ability to be selectively captured,isolated, moved, or sequestered by a selection force. When acategory-binding molecule:selective moiety complex is specifically boundto a target cell, the target cell can also generally be selectivelycaptured, isolated, moved, or sequestered by the selection force.Selective refers to the preferential conferring of susceptibility tomobilization by the selection force on selection moieties and associatedcells over cells not associated with selection moieties.

Paramagnetic particles and ferritin are examples of selection moieties.A dense silica particle that sinks in solution is another type ofselection moiety. Such particles, when coated with category-bindingmolecules and bound to a microbial target cell will cause the targetcell to sink in aqueous solution, thus enabling separation of the boundtarget cell from other sample unbound constituents.

By selective character is meant the aspect or aspects of a selectionmoiety that is useful for capturing, selecting, or moving the selectionmoiety. For example, the selective character of a paramagnetic particleis magnetism. The selective character of a silica particle that rapidlysinks in aqueous solution is mass.

By a roughly planar surface or substrate is meant a surface that can bealigned in parallel to an imaginary plane such that when the distance ismeasured from points in any 1 mm×1 mm square on the surface to theclosest points on the imaginary plane, the absolute value of the meandistance is less than 50 micrometers.

By detection surface is meant the surface of a roughly planar substrateonto which target cells are deposited. In embodiments using photonicsignaling character, if the detection surface is optically transparent,detection can be effected via either face of the detection surface. Ifthe detection surface is opaque, detection is effected via the face ofthe detection surface on which the target cells are deposited.

By detection area is meant the area of the detection surface that issimultaneously sampled by a detection device. For example, the sectionof a glass slide that is simultaneously imaged by an optical device thatincludes a collection lens and a CCD chip might measure 0.8 cm×0.5 cm.The detection area is then 0.4 cm².

By detection zone is meant the volume in which replicating target cellscan be detected by the detection device. The detection zone has the samedimensions as the detection area but has a depth corresponding to thedepth in which the signal from replicating target cells can be detectedand identified. The depth of the detection zone is therefore dependenton the threshold criteria used to score for positive signal. Whenoptical detection is used, the depth of the detection zone is dependenton the optical depth of field.

By the longest dimension of a detection area is meant the line ofmaximum length that can be drawn between two points on the perimeter ofthe detection area. For example, if the detection area is a rectanglemeasuring 0.3 cm×0.4 cm, the longest dimension of the detection area isthe diagonal, 0.5 cm. If the detection area is an ellipse withsemi-major axis of length 7 mm and semi-minor axis of length 2.5 mm, thelongest dimension of the detection area is 14 mm.

By large area detection or large area imaging is meant a method fordetecting microscopic target cells in which the detection area (the areathat is simultaneously analyzed by the detection device) is much largerthan the dimensions of the target cells or microcolonies. The detectionarea for large area detection has at least one linear dimension that is≧1 mm. In contrast, the microscopic colonies are substantially smaller,typically measuring less than 50 μm in at least two orthogonaldimensions. Examples of large area detection include imaging a 9 mmdiameter detection area with a CCD camera; imaging a 2 cm×1 cm rectangleby scanning with a linear array detector that has a long dimension of 1cm; and imaging a 4 cm×4 cm filter using direct exposure on photographicfilm.

Some technologies scan samples for microcolonies but do not exploitlarge area detection. Examples include solid phase laser microbeamscanning cytometry and microscopic examination of multiple high powermicroscopic fields on a slide.

By conjugated or stably associated is meant a physical associationbetween two entities in which the mean half-life of association is leastone day in PBS at 4° C. Consider, for example, the complex case ofpassive protein adsorption to polystyrene particles. There are severaldifferent classes of adsorbed proteins. Some proteins are stablyassociated to the surface with half-lives of many months. Otherproteins, such as those that are loosely bound on the outer layer ofadsorbed protein, may not be stably associated with the particles andcan leach out within hours.

By particle is meant a rigid matrix (i.e., with at least somecharacteristics of a solid), which measures less than one millimeteralong any axis. Particles can be doped with or conjugated to signalelements. Particles are often referred to as particles or with termsthat reflect their dimensions or geometries. For example, the termsnanosphere, nanoparticle, or nanobead are used to refer to particlesthat measures less than 1 micron along any given axis. Similarly, theterms microsphere, microparticle, or microbead are used to refer toparticles that measure less than one millimeter along any given axis.Examples of particles include latex particles, polyacrylamide particles,magnetite microparticles, ferrofluids (magnetic nanoparticles), quantumdots, etc.

By image intensifier or image tube is meant a device that amplifies aphotonic signal, as defined in the glossary of Inoué, Shinya, et al.,Video microscopy: the fundamentals (Plenum Press, New York, 1997; p.665): “A device coupled (by fiber optics or lenses) to a video cameratube to increase sensitivity. The intensifier is a vacuum tube with aphotocathode on the front end that emits electrons according to theimage focused upon it, an electron lens and/or microchannel plate(s)that focuses the electrons onto a phosphor at the back end, and a highvoltage accelerator that increases the energy of the electrons. Can besingle or multiple stage.” A variety of such image intensifiers isdescribed in detail in Chapter 8 of the same reference.

By simultaneous detection in a section of the detection area is meantdetection of the signal from a section of a roughly planar detectionsurface in one step. Large area imaging of targets in a detection areausing a CCD chip, visual detection, or photodiode-based signalintegration are examples of simultaneous detection.

By identification is meant determining the category or categories ofwhich a target cell is a member.

By sample is meant material that is scanned by the invention for thepresence of target cells.

By direct visual detection is meant visual detection without the aid ofinstrumentation other than wearable corrective lenses.

By photoelectric detector is meant a man-made device or instrument thattransduces photonic signals into electric signals. Examples ofphotoelectric detectors include CCD detectors, photomultiplier tubedetectors, and photodiode detectors, e.g., avalanche photodiodes.

By encircled energy or ensquared energy is meant the percentage ofphotons from an infinitely small light source that are captured on apixel of a photodector array.

By thermal radiation is meant black body radiation.

By cellular autofluorescence or autofluorescence is meant thefluorescence exhibited by cells due to the fluorescence of naturalintrinsic cellular constituents, such as NADH and oxidizedflavoproteins. Cells expressing fluorescence due to recombinantfluorescent proteins such as green fluorescent protein are notconsidered to be autofluorescent.

By in situ replication is meant the replication of a target cell inplace, so that the daughter cells remain essentially co-localized withthe progenitor target cell. For example, in in vitro biologicalculturing of bacteria on nutrient agar plates, single dispersed bacteriaare deposited on a plate and incubated under conditions that permitbacterial replication. A bacterium in a certain location replicatesgiving rise to progeny cells that also replicate. All of the cellsremain co-localized (essentially contiguous) with the original cell,eventually giving rise to a visible colony on the plate. Where there wasformerly a single cell, there is now a colony of more than 10⁷ cells.

By a microcolony of target cells is meant a set of target cells that liein close physical proximity to each other, that lie on (or are anchoredto) a surface, and that are the clonal descendants via in situ in vitroreplication-based amplification of a single ancestral target cell. Amicrocolony is generally too small to be visible by the naked eye (e.g.,less than 50 microns in diameter).

Any type of dividing target cell can give rise to microcolonies insituations that lead to physical co-localization of the clonaldescendents of the target cells. For example, microcolonies couldcontain animal or plant cells, fungi, or bacteria.

By illuminating is meant irradiating with electromagnetic radiation.Electromagnetic radiation of various wavelengths can be used toilluminate. It includes, for example, radiation with wavelengths in theX-ray, UV, visible, or infrared regions of the spectrum. Note thatilluminating radiation is not necessarily in the visible range.

By signal elements or signaling moieties with photonic signalingcharacter is meant signal elements or signaling moieties that aredetectable through the emission, reflection, scattering, refraction,absorption, capture, or redirection of photons, or any other modulationor combination of photon behavior. Some examples of signal elements orsignaling moieties that have photonic signaling character include: thefluorophore Texas Red (fluorescent signaling character); CDP-Star(chemiluminescent signaling character); luciferase (bioluminescentsignaling character); resonance light scattering particles (lightscattering signaling character); BM purple (light absorption orchromogenic signaling character); and up-converting phosphors(absorption of two long wavelength photons and emission of one shorterwavelength photon).

By ‘number’ X ‘solution name’ is meant an aqueous solution comprisingthe constituents of solution name at number times the concentration ofthe solution (except for water). For example, 10×EE contains 10 mMEDTA/100 mM EPPS (EE, or 1×EE, contains 1 mM EDTA/10 mM EPPS).

-   EE is a solution that is 1 mM EDTA/10 mM EPPS. Before mixing them    together, the conjugate acids of both components are brought to pH    8.0 with NaOH-   PB is 0.1 M sodium phosphate buffer pH 7.4.-   PBS is a phosphate-buffered saline solution containing: 120 mM NaCl,    2.7 mM KCl and 10 mM phosphate buffer (sodium salt) pH 7.4.-   PBS-B is 0.1% BSA (IgG Free; Sigma Cat. No. A-7638) in PBS.-   PBS-T is 0.05% Triton X-100 (Sigma Cat. No. X-100) in PBS-   PBS-TB is PBS/0.1% BSA/0.05% Triton X-100-   PBT is PBS/0.1% BSA (IgG Free; Sigma Cat. No. A-7638)/0.05% Tween-20    (Sigma Cat. No X-100)-   LB is Luria Broth for growing bacteria and is made as described    previously (Ausubel 1987, supra).-   SSC is 150 mM NaCl/15 mM Na₃ citrate adjusted to pH 7.0 with HCl.-   EDAC is (1-Ethyl-3-(3-dimethylaminopropyl)) carbodiimide.-   TSA is Tryptic Soy Agar (Becton Dickinson/Difco; cat. num. 236950).-   TSB is Bacto™ Tryptic Soy Broth (Becton Dickinson cat. num. 211822).-   AP is alkaline phosphatase.-   BSA is Bovine Serum Albumin.-   CCD is charged coupled device.-   Cfu is Colony forming unit (a measure of bacterial concentration    that corresponds to the number of viable bacterial cells).-   FITC is fluorescein isothiocyanate.-   PNA is peptide nucleic acid.

Unless otherwise noted, microbiological strains described in thespecifications are obtained from the American Type Culture Collection(ATCC), Manassas, Va.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1. Traditional Microbial Culture Requires Many Generations of CellDivision.

The long time-to-results of traditional microbial culture results fromthe time required to generate enough microscopic target cells to bevisible to the naked eye.

FIG. 2. The Concept for Rapid Detection of Microbial Growth by DetectingMicrocolonies

The invention achieves rapid enumeration of growing cells by imagingmicrocolonies containing fewer cells than do the macrocolonies that aredetected by eye using the traditional method. The invention is fasterbecause fewer generations are required than for the traditional method

FIG. 3. A CCD Imaging Device for Large Area Imaging

The CCD-based imager depicted in the figure was used to collect much ofthe data described in the examples (see also Step 5 of DetailedDescription section). In one example, excitation light is provided byintroducing light from a high intensity white light source (1000 WattXenon arc lamp, Model A-6000, Photon Technology Incorporated, MonmouthJunction, N.J.) into a liquid light-guide (5 mm core diameter, Model380, Photon Technology Incorporated, Monmouth Junction, N.J.). Theliquid light-guide carries the light to an excitation filter-wheel(BioPoint FW, Ludl Electronics, Hawthorne, N.Y.) and directs thefiltered beam (typically 9 mm in diameter) onto the detection surfacecontaining the labeled target cells. The detection surface is theoptically clear bottom of a microtiter dish well. However, the sameapparatus can detect labeled target cells on various detection surfaces(e.g., microscope slides, coverslips, and tubes with flat, opticallyclear, bottoms). The incident light strikes the detection surfaceinducing fluorescence in the signaling moieties that are bound to targetcells via category-binding molecules and that are deposited on theoptically clear surface. A portion of the emitted fluorescent light iscollected by a high-collection efficiency lens system and transmittedthrough an emission filter-wheel (BioPoint FW, LudI Electronics) to aCCD Camera (Orca II, Hamamatsu, Bridgewater, N.J.).

FIG. 4. A CCD Imaging System for Non-Magnified Large Area Imaging

The figure shows a CCD imager with an angular illumination configurationin which light is introduced onto the detection surface (shown here asthe bottom of a well of a microtiter plate) at an angle from the side ofthe collection optics. The angle is chosen to optimize collectionefficiency and to avoid obstruction of the incident beam by thecollection lens. The advantage of this configuration is that reflectionsfrom the bottom surface of the sample holder are not collected by thecollection lens and therefore do not contribute to the fluorescencebackground noise.

FIG. 5. Reagent-Less Detection of Microcolonies Using Non-MagnifiedLarge Area Imaging.

The figure diagrams a rapid method for enumerating bacterial growthwithout using a labeling reagent. The intrinsic autofluorescence oftarget cells in microcolonies is detected using CCD-based non-magnifiedlarge area imaging. Advantages of this reagent-less approach include itssimplicity, non-destructiveness, and broad applicability. Alternatively,labeling reagents that bind to target cell-specific binding sites (e.g.,fluorescent antibodies or nucleic acid probes) can be used for detectingmicrocolonies containing target cells.

FIG. 6. Detection and Identification of Bacterial Microcolonies UsingNon-Magnified Large Area Imaging (Example 1)

The figure shows a rapid, simple, and sensitive method for detectingmicrocolonies by imaging labeled microcolonies using CCD-basednon-magnified large area imaging. In this example, single cells wereallowed to go through several replicative generations in order to formmicrocolonies. The microcolonies were labeled with either Syber Green Ior a FITC-labeled antibody. In FIG. 7 the upper row of panels shows the0 hour time point containing single cells. The lower row of panels showsmicrocolonies after 3 hours of incubation. There is a substantialincrease in size and signal of the objects detected by CCD imaging overtime due to the increase in the number of cells at the sites where thecolony-forming cells were originally deposited.

FIG. 7. Autofluorescence-Based Detection of Bacterial MicrocoloniesUsing Non-Magnified Large Area Imaging (Example 2)

The figure diagrams a rapid, simple, and sensitive method for detectingmicrocolonies by imaging cellular autofluorescent signals usingCCD-based non-magnified large area imaging. Single dispersed cells weredeposited on a filter, which was incubated on growth medium for 5.25hours at 37° C. Microcolonies (resulting from the clonal growth of thesingle dispersed cells) generated substantial autofluorescent signal(left panel) when compared to a filter on which no bacteria weredeposited (right panel) but that was otherwise prepared and imagedidentically.

FIG. 8. A Simple Method for Validating a Rapid Reagent-Less MicrobialEnumeration Test Using an Internal Comparison to the Traditional CultureMethod (Example 3)

The figure demonstrates a simple method for showing the equivalence ofmicrocolony enumeration to the traditional method. Using non-destructivedetection of microcolony autofluorescence allows the microcoloniesdetected by the invention to be re-incubated until they mature into themacrocolonies that are detected using traditional visible colonycounting. Note that the pattern of spots formed by the microcolonies(left panel) matches the pattern formed by the visible colonies (rightpanel) indicating the equivalence of the two methods.

FIG. 9. Accuracy and Limit of Detection of Autofluorescent MicrocolonyDetection Using Non-Magnified Large Area Imaging (Example 4)

The figure shows the method used to measure the accuracy of theinvention when the samples contain extremely low levels of target cells.For each of the 101 filters, the result obtained by scoring theautofluorescent microcolonies was the same as the result obtained by thetraditional method.

FIG. 10. Determining the Number of Microbial Cells in AutofluorescentBacterial microcolonies rapidly detected using reagent-lessnon-magnified imaging (Example 5)

The figure shows the signal generated from microcolonies of E. coliusing large area imaging from Escherichia coli microcolonies (toppanel). The three microcolonies imaged with high powered microscopy inthe bottom panels correspond to the three microcolonies imaged using theinvention in the upper panel. The number of bacteria in each microcolonyis indicated below each frame (45, 48 and 50 cells). The figuredemonstrates that microcolonies containing low numbers of E. coli cellscan be detected using reagent-less non-magnified large area imaging.

FIG. 11. CCD-Based, Non-Magnified, Large Area Imaging Detection andIdentification of Bacterial Microcolonies in an Environmental WaterSample (Example 6)

The figure shows the analysis of bacterial growth by using the inventionto detect bacterial colonies in water from the Charles River. Bacterialcells were collected onto mixed cellulose ester filters. The filterswere placed onto an R2A agar plate, and incubated for 74 hours at 32.5°C. At various time points the filters were imaged using reflectance ofwhite light and autofluorescence. Macrocolonies that were 0.55 mm orgreater in diameter were identified and counted in the reflectanceimages. The time points at which autofluorescent microcolonies that gaverise to a macrocolonies could be detected was also determined. Atvarious time points the percentage of the 74 hr macrocolonies that weredetectable as autofluorescent microcolonies was plotted.

FIG. 12. Correlation Between CCD-Based, Non-Magnified, Large AreaImaging Detection of Bacterial Microcolonies and a Classical Pour PlateCulture Method for Enumerating Bacteria in a Sample (Example 7)

The figure compares the enumeration of autofluorescent microcoloniesobtained using the invention and the traditional pour plate method ofmicrobial culture.

FIG. 13. Dynamic Range and Linearity of a Reagent-Less Enumeration Test(Example 8)

The figure shows the analysis of dynamic range and linearity by usingthe invention to detect autofluorescent microcolonies.

FIG. 14. Antimicrobial Preservative Effectiveness Testing without SampleDilutions (Example 9)

The figure shows that comparable antimicrobial preservativeeffectiveness results are obtained using invention and traditionalmethods. The comparison shows the potential of the invention toeliminate most of the labor and expense of this test by obviating theneed to analyze hundreds of sample dilutions.

FIG. 15. Autofluorescence-Based Detection a Heat-Stressed BiologicalUsing Non-Magnified Large Area Imaging (Example 10)

The figure shows the correlation between enumeration of heat-stressedbiological indicator cells using the invention and the traditional pourplate method. The biological indicator G. stearothermophilus wassubjected to a variety of heat stress regimes. Microcolonyautofluorescence was measured using CCD-based large area imaging andvisible macrocolonies were counted visually. The results of the twomethods are plotted against each other and show good correlation. Theinvention, however, required substantially fewer dilutions than did thetraditional method.

FIG. 16. Autofluorescence-Based Detection of Bacterial Microcolonies inGround Beef (Example 11)

The figure shows the detection times of autofluorescent microcoloniesand macrocolonies derived from microbes in ground beef. Tracking theappearance over time of microcolonies that gave rise to the 48 hrmacrocolonies showed that 100% of the macrocolonies were detected by theinvention at 16 hrs. This shows the potential of the invention to reducethe time required to achieve results significantly compared totraditional methods.

FIG. 17. Magnetic Selection Followed by Microcolony Detection (Example12)

A scheme is shown for magnetic selection of target cells followed by insitu growth and detection of microcolony autofluorescence using theinvention.

FIG. 18. Detection of Bacteria in a Complex Sample with Non-SpecificMagnetic Selection Followed by Microcolony Detection Using Non-MagnifiedLarge Area Imaging (Example 12)

The figure shows results of an experiment in which S. aureus bacteriawere magnetically captured from whole blood. The bacteria were selectedfrom a blood sample using magnetic particles coated with a mixture ofbroadly reactive agents that bind bacteria. After filtration, plating,and incubation (6 hr), the autofluorescent microcolonies were detectedusing non-magnified large area imaging. The filters were allowed toincubate overnight. Afterwards, the filters were again imaged (imagesnot shown) and the position of six hour microcolonies were verified tohave grown into macrocolonies, eliminating the chance that themicrocolonies would have been mistaken for dust or other particulates.

FIG. 19. Scheme for Rapid Antimicrobial Susceptibility Testing (Example13)

The figure diagrams a rapid method for testing the sensitivity of abacterial strain to an antibiotic by detecting the appearance ofmicrocolonies using CCD-based non-magnified large area imaging. For thestrain of bacteria shown, microcolonies cannot form when the bacteriaare grown in the presence of the antibiotic (right column) indicatingsensitivity to the antibiotic. Bacteria also do not grow withoutincubation under growth conditions (left column). As expected, growth isdetected when the strain is incubated under growth conditions in theabsence of the antibiotic (center column).

FIG. 20. Rapid Antimicrobial Susceptibility Testing (Example 13)

The figure shows the results of an antimicrobial susceptibility testthat compares the growth of bacterial strains (one sensitive to and oneresistant to the antibiotic tetracycline) as microcolonies on agarplates containing the antibiotic. Bacterial cells from each strain werefiltered onto a polycarbonate membrane, placed onto LB agar platescontaining tetracycline, and then incubated for three hours at 37° C.(columns labeled “3 hour”). Other filters prepared similarly were placedon LB agar plates containing tetracycline for less than 5 minutes atroom temperature (panel columns labeled “0 hour”). The filters werefixed and stained with a nucleic acid stain. CCD imaging of themembranes containing bacteria that were incubated for three hours(column labeled: “3 hour CCD”) detected microcolony growth on themembranes that contained the resistant strain but not the sensitivestrain. The growth of microcolonies on the filters containing theresistant but not the sensitive strain was confirmed by high powerfluorescence microscopy (column labeled: “3 hour microscope”). Asexpected, no microcolonies were detected on the CCD image of the filtersthat were not incubated under growth conditions (column labeled: “0 hourCCD”) and only single dispersed cells were detected by high powerfluorescence microscopy. Computer image analysis was used to quantifythe results of CCD imaging of the membranes (bar graph). The membranecontaining microcolonies formed by the resistant strain generated about25-fold more intensity than did the membrane containing the sensitivestrain. The results of this experiment show that detecting microcoloniesusing non-magnified large area imaging is a rapid and sensitive methodfor antimicrobial susceptibility testing.

FIG. 21. Rapid Antimicrobial Susceptibility Testing Using the DiskDiffusion Method and Non-Magnified Large Area Imaging (Example 14)

The figure shows the results of an antimicrobial susceptibility diskdiffusion test comparing the growth of bacterial strains (one sensitiveand one resistant). Autofluorescent microcolonies, growing on or nearthe diffusion disk, were detectable after 5 hours of growth, greatlyreducing the time to detection from a standard overnight growth. Theleft panel shows the Tet resistant strain growing close to the diffusiondisk, while the right panel shows the lack of growth of the Tetsensitive strain. The disk diffusion plates were allowed to incubateovernight. The 24 hour zones of inhibition were compared to the 5 hourzones. The 24 hour zone of inhibition was the same as the 5 hourmicrocolony result indicating that the invention can yield faster butcomparable results compared to the traditional method.

FIG. 22. Rapid Antimicrobial Susceptibility Testing Using the E-Test™and Non-Magnified Large Area Imaging (Example 15)

The figure shows the results of an antimicrobial susceptibility testcomparing the growth of bacterial strains (one sensitive and oneresistant) using E-test™ strips containing the antibiotic tetracycline.Bacterial cells were spread on TSA plates. An E-test™ strip was addeddirectly to the plates, which were incubated at 37° C. Autofluorescentmicrocolonies growing on or near the E-test™ strip were detectable after5 hours of growth. The left panel shows the resistant strain at 5 hoursgrowing near the 256 μg/ml segment of the strip. The right panel showsthe sensitive strain at 5 hours with a zone of inhibition near the 2μg/ml segment of the strip. The E-Test™ plates were allowed to incubateovernight. The 24 hour zones of inhibition were comparable to the 5 hourzones indicating that the more rapid results obtained with the inventionare comparable to the slower traditional method.

DETAILED DESCRIPTION OF THE INVENTION

Overview of the Invention

The invention rapidly and cost-effectively analyzes a minimallyprocessed sample for growing cells. Both the invention and traditionalbacterial culture methods measure cell growth by detecting the formationof bacterial “colonies”—clusters of associated cells that arise fromsingle cells via successive cell divisions (FIG. 1). However, theinvention detects cell growth more quickly than traditional microbialculture, because it detects microcolonies that appear at an earlierstage than the visually observed macrocolonies detected by traditionalmicrobial culture (FIG. 2, FIG. 8). By using the same method principlesas microbial culture the invention can retain the advantages of thetraditional method while still improving time to results significantly.

To understand how the invention detects microcolonies it is helpful toexamine a specific embodiment and application. For example, consider atest for enumerating the microbes in the water used to manufacture aninjectible pharmaceutical. The microbes in a sample of the water (100ml) are concentrated and immobilized by passing the liquid through aporous membrane. The membrane containing the microbes is placed onnutrient growth medium in a disposable petri dish. The microbes areincubated at 32° C. to allow them to replicate and to formmicrocolonies. Light is directed at the surface of the membrane causingcells in the microcolonies to autofluoresce. This autofluorescent signalderives from biomolecules that are present in the cells (e.g., NADH andoxidized flavoproteins). The autofluorescing microcolonies are thenimaged electronically. Light originating from an individual microcolonystrikes a pixel or small cluster of adjacent pixels on a CCD arrayphotodetector. The number of autofluorescing microcolonies isimmediately calculated by image processing software and reported to theuser. Note that the process is identical to traditional microbialculture, except that the invention detects the results faster andautomatically.

Because this embodiment of the invention is non-destructive (i.e., doesnot kill or injure the microbes), the detected microcolonies can begrown into pure cultures. These pure cultures can be used for microbialidentification—and, for clinical samples, for determining antimicrobialresistance and susceptibility. Non-destructive detection also makes itsimple to validate the equivalence of the method to traditionalmicrobial enumeration. After detecting the microcolonies using theinvention, the petri dish can simply be re-incubated to allow themicrocolonies to growth for the length of time required to generate thevisible macrocolonies detected by traditional microbial culturedetection. Comparing the number and location of microcolonies detectedby the invention to the visible colonies derived from further growth ofthe microcolonies facilitates determining the equivalence of theinvention and the traditional method.

The invention can be used to construct tests using a number of formats,labeling methods, category-binding molecules, and detection methods.However, the tests have several key features in common. The steps andprocesses that are common to many embodiments of the invention aredescribed below.

The General Configuration of Applications of the Invention Includes theFollowing Steps:

-   Step 1: Formulating the test question, choosing the sample,    categories of cells to be detected, growth conditions, and signaling    character-   Step 2: Depositing the cellular targets in the detection area-   Step 3: Allowing cellular replication to form microcolonies-   Step 4: Optional labeling of microcolonies-   Step 5: Enumerating the microcolonies

Formulating the question to be answered by the test is the first step increating a new application based on the invention. Some examples ofimportant questions that industrial and clinical microbiologists mustaddress are listed in Table 3. Articulating the test question generallydefines the sample type that must be tested (e.g., ground beef, clinicalurine sample, or a pharmaceutical finished product). The sample type andvolume are important parameters in choosing methods for depositing thetarget cells in the detection area (see step 2). Articulating the testquestion also defines the types, or categories, of cells that must bedetected in the application (e.g., aerobic bacteria, yeast or molds,pseudomonas, E. coli O157:H7, or an anonymous spinal fluid isolate).

TABLE 3 Examples of questions answered by tests based on the inventionDo the numbers of bacteria in a urine sample indicate a urinary tractinfection? Does a patient's blood sample contain viable infectiousmicrobes? Which antibiotic is best for treating a particular patientwith bacterial meningitis? How many aerobic bacteria are present in 25 gof meat? Are there any cells of the foodborne pathogen E. coli 017:H7 ina sample of ground beef? How many yeast and mold cells are present in anenvironmental air sample? How many Pseudomonas cells are present in 10 gof an over-the- counter pharmaceutical tablet? Is the finished productbatch of injectible drug sterile? How many yeast cells are present in aproduction sample of beer?

After defining the type of cells to be detected or enumerated,conditions are chosen for fostering the growth of the cells in thedetection area. Important parameters for allowing cellular replicationinclude: composition of the growth medium, presence of selectivereagents such as antibiotics, temperature, and the level of oxygen andother gases. If possible, growth conditions are chosen that fostergrowth of the cells to be detected but that are refractory to the growthof other types of cells. For example, media for detecting yeast andmolds often contain ingredients that inhibit the growth of otherwisemore rapidly growing bacterial microbes.

A method for generating detectable signal from the cells to be detectedmust also be chosen. Choosing the signal depends on the type of cells inthe microcolonies to be detected, the types of other cells that mightform microcolonies, and the type of background expected in the sample.Consider a test for determining the total number of aerobic bacteria ina finished product in pharmaceutical manufacturing; a wash solution forcontact lenses, for example. Because a broad spectrum of thousands ofenvironmental microbes could be present in such a sample, the signalgenerating method must be very general. Some such methods rely on theintrinsic optical properties of the microcolonies, such as microcolonyautofluorescence, reflectance, or infrared absorbance. Such methodsallow rapid microcolony detection without using a reagent—an importantadvantage of the invention. Reagent-less signal generation using, forexample, microcolony autofluorescence, substantially simplifies testmethods, allows aseptic sample processing, and enables rapid tests thatuse the same media and disposables used in “gold standard” methods.Alternatively, microcolonies generated by the target cells can belabeled using stains.

Using Stains and Specific Probes to Enumerate Specific Categories ofTarget Cells

Using stains or probes that bind to molecular constituents of targetcells can be used in applications that ask a range of diagnosticsquestions. Examples of stains that can be used to detect a broad rangeof target cells (e.g., all aerobic bacteria) include nucleic acid stains(e.g., propidium iodide or Syber Green (Molecular Probes)), and stainsfor enzyme activity (e.g., fluorogenic esterase stains). To labelnarrower categories of target cells, labeled probes that bind totarget-specific molecular constituents can be used. For example, afluorescently labeled antibody that binds specifically to a moleculethat only occurs on the surface of the food pathogen E. coli O157:H7 canbe used to detect pathogenic microcolonies in a food sample.

Thus, to detect the presence of a category of target cells, theinvention can use molecules that bind specifically to category-specificmolecular constituents. The category-specific molecular constituentsthat occur on target cells are called category-specific binding sitesand the molecules that bind specifically to them are calledcategory-binding molecules. To detect the binding of category-bindingmolecules, a detectable label, or signaling moiety is generally attachedto the category-binding molecules. Note that category-specific bindingsites are a property of target cells that are potentially present in thesample to be tested. In contrast, category-binding molecules are areagent provided in a diagnostic test kit.

An advantage of the invention is that a broad spectrum ofcategory-binding molecules can be used. This feature is important sincedifferent types of category-binding molecules are used to ask differenttypes of diagnostic questions (e.g., broad kingdom-level screening vs.narrow subspecies-level identification). Classes of category-bindingmolecules (also sometimes referred to as probes) comprise: nucleic acids(oligonucleotides, aptamers, cloned sequences, genomic DNA, RNA, etc.);chemical variants related to nucleic acids, such as peptide nucleicacids (PNA); antibodies; enzymes (which can bind target substrates);non-enzymatic proteins such as avidin (which binds the target moleculebiotin); molecules that bind cellular constituents specifically (e.g.,phalloidin which binds actin or biotin which binds avidin); dyes andstains, (e.g., propidium iodide, auramine-rhodamine, or SYTO 17);ligands (e.g., epidermal growth factor, which binds specifically to theepidermal growth factor receptor); and polypeptide or nucleic acidbinding reagents that have been selected using in vitro evolutiontechniques (e.g., Zhang, et al., Nat. Biotech. 18: 71-74, 2000).

Category-binding molecules can incorporate other functional domains ormodifications. For example, category-binding molecules are oftencovalently or non-covalently associated with signaling moieties (i.e., alabeling domain such as a fluorophore or a dyed microparticle) orselection moieties (e.g., magnetic particles or solid surfaces).Alternatively, a category-binding molecule may be linked to an adaptormoiety that, in turn, facilitates linkage to another functional moiety.Sometimes the category-binding molecule has dual non-separablefunctions. For example, propidium iodide, a nucleic acid stain, can beused as a category-binding molecule (e.g., the category-specific bindingsite might be the cellular nucleic acid in a yeast) while, at the sametime, the bound dye functions as a signaling moiety (i.e., it canfluoresce when bound to the category-specific binding site). Tests basedon the invention can incorporate more than one class of category-bindingmolecule (e.g., antibodies and nucleic acid stain, or antibodies andoligonucleotides).

The simplest tests incorporate a single type of category-bindingmolecule to scan for a single category of target cell. For example, atest for M. tuberculosis might use a monoclonal antibody that bindsspecifically to a category-specific binding site on the surface of M.tuberculosis. In another example, when screening for urinary tractinfections, the single category is “all cells”—or, if human cells arelysed, “all non-human cells”—and the single type of category-bindingmolecule could be a nucleic acid stain (e.g., propidium iodide).

A family of category-binding molecules is a set of distinctcategory-binding molecules that bind to members of the same category oftarget cell. For example, a polyclonal antibody raised to Hepatitis Cvirus is a family of antibodies since it comprises multiplecategory-binding molecules that bind specifically to the same categoryof target cell—in this case HCV. Another example of a family ofcategory-binding molecules is a set of 80 category-specific genomic DNAsequences that occur in all E. coli O157: H7 strains but do not occur inmembers of other groups of bacteria. This family of category-bindingmolecules can hybridize as a group to suitably prepared E. coli O157:H7cells but does not hybridize to other types of cells.

To detect multiple categories of target cells, a test includes onefamily of category-binding molecules for each category. A set offamilies of category-binding molecules is called an ensemble ofcategory-binding molecules. For example, tests for pneumonia or testsfor drugs of abuse, must distinguish numerous categories of target cellsfrom each other. One family of category-binding molecule is used foreach category of target cell. For a pneumonia test, an ensemble ofantibodies that react to category-specific antigens on the surface ofmicrobes that cause pneumonia might be used. One family in thiscategory-binding molecule ensemble might comprise polyclonal antibodiesfrom the immunoglobulin fraction of antiserum raised in a rabbit hostand directed against Streptococcus pneumoniae. Another family couldcomprise a recombinant antibody or a monoclonal antibody directedagainst a coat protein of adenovirus.

The number of distinct groups or categories of target cells tested forby an ensemble, i.e., the categorical complexity, is reflected by thenumber of families of category-binding molecules in the ensemble. Thenumber of families in an ensemble can, in turn, be accurately defined bya quantity called the “minimum categorical derivation” of an ensemble.The family complexity is the minimum number of distinct target cellsrequired to bind members from each of the families of category-bindingmolecules in the test ensemble. For example, consider an ensemble ofcategory-specific antibodies used to simultaneously test a sputum samplefor the presence of Mycobacterium tuberculosis, Legionella spp, andCoccidoides immitus. The family complexity of the ensemble would bethree, since a minimum of three target cells, one from each pathogencategory, would be required to bind to members of each family ofcategory-binding molecules in the ensemble. The ability of the inventionto identify a broad spectrum of target cell categories in a single testis a consequence of its ability to scan a sample using an ensemble ofcategory-binding molecules that has a large family complexity.

Category-binding molecules used in conjunction with the invention shouldbe specific in that they should bind under assay conditions to thedesired target cell but not to other types of target cells meant to bedistinguished by the assay or to other possible constituents of thesample or test. Thus, in a test for upper respiratory bacterialinfection, potential category-binding molecules are screened toeliminate those that cross react with normal (commensal) microbialconstituents of the upper respiratory tract.

Representative methods for obtaining and characterizing category-bindingmolecules are included in the examples below.

The invention's ability to analyze a sample for numerous disparatecategories of target cells simultaneously derives from the ability todifferentiate the signals derived from the different categories oftarget cells. The invention discriminates between categories by labelingeach category-specific family of category-binding molecules withsignaling moieties such that it has a unique signal signature. Theability to generate and detect large numbers of distinct signalsignatures (i.e., high signal complexities) enables construction oftests that analyze for numerous categories of target cells (i.e., highlymultiplexed tests).

The invention can exploit various types of signal character including:fluorescence, scattered light, light polarization, chemiluminescence,and radioactivity. Examples of signaling moieties and detection schemesusing various signal characters appear below. There can be multiplesignal classes within a signal character. For example, if the signalcharacter is fluorescence, various characteristic emission spectra areincluded in the signal classes (e.g., red fluorescence, greenfluorescence, and blue fluorescence). In another example, consider redfluorescent microparticles that are dyed with different concentrationsof the same fluorophore. In this case, fluorescence is the signalcharacter, but the different intensities of the particles constitute theclasses of signal character, i.e., fluorescence intensity is the qualityof the signal character that differentiates one group of particles fromanother.

A great variety of signaling moieties can be used in conjunction withthe invention as demonstrated in the examples below. Signaling moietiescan include simple fluorophores, up-regulated phosphors, naturallyfluorescent proteins (such as green fluorescent protein and itsrelatives), dyes, enzyme:substrate systems (generating color changes orchemiluminescence), fluorescent microparticles, light scatteringparticles, magnetic particles, or radio transmitting microdevices.

Attaining a high signal complexity is key to developing certain teststhat scan for numerous types of target cells (i.e., tests with highcategorical complexity).

Achieving High Signal Complexity

The number of distinguishable labels (or signaling moieties) in amixture is called the signal complexity. For highly multiplexed tests,it is sometimes advantageous to use signaling moieties with high signalcomplexity. Three general approaches that can be used with thisinvention to generate high signal complexity are: (1) distinct labeling,(2) combinatorial labeling, and (3) ratio labeling.

-   1. For distinct labeling, probes in different probe families are    tagged with a single signaling moiety that can be readily detected    in the presence of all other signaling moieties in the experiment.    Thus far, it has been difficult to achieve distinct labeling at high    signal complexities. This difficulty is present because most    labeling methods use optical signals (e.g., chromogenic,    fluorescent, chemiluminescent) or radioactive labeling, and because    of the spectral bandwidth of optical signals and the limited range    of signals detectable by current instruments, the resolvable signal    complexity using optical signals is rather small. For example, the    resolution of dozens of fluorophores with distinct emission spectra    is currently impossible because of spectral overlap.-   2. Another way to achieve the high signal complexity used in the    invention is to apply a combinatorial labeling approach.    Combinatorial labeling is a technique for achieving high signal    complexity using a relatively small number of distinct signaling    moieties. With this approach, distinct combinations of signaling    moieties are bound to different targets. Currently, fluorophores are    a favored class of signal moiety for molecular diagnostics. However,    given the complications involved in analyzing multiple distinct    fluorophores (arising in large part from overlap of the excitation    and emission spectra), it is only currently practical to incorporate    about seven or fewer conventional fluorophores. However, used in    combination, seven fluorophores can be used to generate 127 distinct    signals (N fluorophores generate 2^(N)−1 combinations). High signal    complexity can also be achieved via combinatorial labeling using    other types of signaling moieties. For example, particles    impregnated with different dyes, particles that fall into different    discrete size classes, or transponders emitting distinct radio    signals could be used with this approach. Combinatorial labeling    using fluorophores has recently been applied with success for human    karyotyping (Speicher et al 1996, supra; Schrock et al 1996, supra).    Instrumentation and software for analysis of combinatorial labeling    experiments is commercially available.-   3. High signal complexity can also be obtained using the ratio    labeling approach (Fulton, et al 1997, supra). In ratio labeling, as    in combinatorial labeling, many distinct types of signaling moieties    are generated using a relatively small number of distinct signaling    elements. However, in contrast to combinatorial labeling, the    signaling moieties in ratio labeling are distinguished by the ratio    of the signaling elements. For example, two fluorophores, X and Y,    with different excitation/emission characteristics can be used to    dye polystyrene particles. Different relative concentrations of the    fluorophores ([X], [Y]) are applied to different sets of particles.    For example, eight different concentrations of X and eight different    concentrations of Y can be used to dye particles in all combinations    (X₁Y₁, X₁Y₂, X₈Y₇, X₈Y₈) resulting in 64 classes of distinguishable    particles. Ratio labeling simplifies instrumentation, as only a    small number of signal types need be used. Signal elements, other    than fluorophores and including non-optical signal elements, can    also be used to generate high signal complexities using a ratio    labeling approach.    Generating Strong Signals to Facilitate the Detection Microcolonies

The level of signal intensity needed is, of course, dependent on thetype of signal character and the detection method/instrumentation (seebelow).

Various approaches for labeling category-binding molecules can be usedto achieve the required sensitivity. One method for optimizing thesignal strength is to label target molecules with highly fluorescentsignaling moieties. For example, quantum dots, fluorescently dyednanospheres, and polymerized fluorophore molecules generate strongfluorescent signals. Incorporating numerous signal elements can increasethe fluorescence intensity of a signaling moiety. For example,fluorescent nanospheres (˜20 nm in diameter; Molecular Probes) cangenerate a signal equivalent to about 180 fluorescein molecules.Fluorescently dyed polystyrene microparticles (e.g., 1 μm in diameter)can incorporate millions of fluorophore signaling elements. A method forincorporating multiple fluorophores in a signal moiety associates with anucleic acid category-binding molecule is to incorporatefluorophore-dNTP conjugates during PCR amplification of a clonedcategory-specific sequence. Alternative methods for incorporatingmultiple fluorophores into nucleic acid category-binding moleculesinclude approaches using: dendrimers, branched DNA, or rolling circletemplates bound to multiple signal moieties, or tailing with numerouspolymerized fluorophore labeled nucleotides. Conjugatingcategory-binding molecules to multiple signaling moieties also increasessignal intensity. For example, signal amplification can also be achievedby conjugating large numbers of signaling enzymes (e.g., alkalinephosphatase or horseradish peroxidase) to a nanoparticle.

Another approach to obtain strong signals is to bind numerous labeledcategory-binding molecules to each cell. This binding can be achieved byvarious means including: using multiple category-binding molecules(recognizing multiple category-specific binding sites in the same targetcell) or by choosing category-binding molecules that bind to targetmolecules that are highly represented in a target cell. For example, alabeled microbe-specific polyclonal antibody can achieve high signalintensities by binding to numerous distinct epitopes on a microbialtarget cell. The strategy of choosing category-specific binding sitesthat are present in large numbers in each target cell has beenfrequently used previously. Examples of this strategy include the use ofnucleic acid probes for ribosomal RNA (which depending on the targetorganism and cell type can be present in thousands of copies per cell).Similarly, some antigenic target molecules are present in hundreds orthousands of copies in each cell of a target organism. The invention canexploit both of these strategies. As another example, the large numberof category-specific binding sites present in a bacterium yield strongsignal intensity when using the nucleic acid-binding fluorescent dyeSyber Green I as the category-binding molecule/signaling moiety.

Binding numerous signal moieties to a target cell not only increasessignal strength, but it endows the invention with robustness since thechances are small of observing numerous clusters of a large number ofsignaling moieties with the expected composite signal signature in theabsence of the target cell.

Conjugating Signaling Moieties to Category-Binding Molecules

The invention can incorporate numerous types of signaling moieties whichcan be directly conjugated to category-binding molecules using variousmethods which are known by those familiar with the art (see, forexample, Hermanson, G., Bioconjugate Techniques (Academic Press, 1996)and specific examples below). For example, antibody or oligonucleotidecategory-binding molecules can be directly conjugated to a fluorophoreor a quantum dot signaling moiety. Alternatively, antibodies oroligonucleotide category-binding molecules can be used to coatfluorescent microparticle-based or light-scattering nanoparticle-basedsignaling moieties. Signaling moieties can be indirectly conjugated tocategory-binding molecules. For example, avidin can be directlyconjugated to multiple signal elements to constitute a signaling moiety.The labeled avidin molecule can then be bound to a biotinylatedcategory-specific antibody. Signaling moieties can be conjugated to thecategory-binding molecules before, during, or after the binding steps.For example, in one embodiment of the invention, digoxygenin-labelednucleic acid probes are used as the category-binding molecules. Afterbinding the category-binding molecules to the category-specific bindingsites in the target cells in the sample, the unbound probes are washedaway. Anti-digoxygenin antibody:alkaline-phosphatase conjugates (thesignaling moieties) are then conjugated to the bound digoxygenin-labeledprobes. An alkaline-phosphatase substrate (e.g., the chemiluminescentsubstrate CDP-Star; NEN)) is then added to the boundalkaline-phosphatase to generate the signal.

Step 2: Depositing the Cellular Targets in the Detection Area

Depositing the target cells in the sample in the detection zone isgenerally the next step in applications based on the invention.Essentially planar detection zones are often used, in part, becauseoptical imaging systems can efficiently collect light from thindetection zones (i.e., optical systems with a small depth of field), forexample, when microcolonies are grown on the surface of the nutrientagar or on membranes lying on the surface of nutrient agar plates. Inthese cases, the depth of the detection zone can be negligible comparedto the lateral dimensions of the detection zone. This step can also beused to deposit certain target cells selectively, to remove substancesthat might inhibit cell growth, or to contact target cells with labelingreagents.

Using membrane filtration to deposit cells on a roughly planar membranedetection surface has several advantages. The ability to collect smallnumbers of target cells from large sample volumes is one importantadvantage of using membrane filtration. For example, a single bacterialcell in 1 liter of water can be quickly and efficiently deposited on thesurface of standard filtration membranes. Water can pass freely throughthe membranes but cells can not, because of the size of the membrane'spores. The water sample is poured into a container the base of whichformed a membrane and then a vacuum is applied to the bottom surface ofthe membrane. Water is drawn through the membrane while cells areretained on the membrane surface. The membrane can be optionally washedwith liquid to efficiently remove substances such as growth inhibitorsor to expose cells to labeling reagents. The membrane can then be placedon growth media.

Other methods for depositing the target cells on a surface includecentrifugation, gravitational settling, magnetic selection, or bindingto surface bound category-binding molecules (e.g., capture antibodies).In some cases (e.g., magnetic separation) a distinct moiety, theselection moiety is used. Magnetic microparticles coated withcategory-specific antibodies are an example of a selection moiety. Aftertarget cells are allowed to bind to the antibody-coated magneticparticles, a magnetic field is applied to deposit the magneticallylabeled cells on the detection surface. Similarly, dense microparticlescoated with target-specific antibodies can be used as selectionmoieties. In this case, the labeled cells are brought to the detectionsurface by the action of gravity on the dense particles.

Step 3: Allowing Cellular Replication to Form Microcolonies

In this step, target cells form microcolonies by dividing in place inthe detection zone. Microcolony growth is supported by exposing cells togrowth medium containing nutrients and incubating them under conditionsthat foster cell growth and division (these parameters are selected inStep 1 above). In a typical embodiment, cells are deposited on a porousmembrane filter. The filter is placed on the surface of solidifiednutrient agar growth medium in a petri dish, which is then covered andplaced in an incubator set at the appropriate temperature. This methodis currently used widely to support colony growth using traditionalmicrobial culture because nutrients can diffuse freely through themembrane without causing movement of daughter cells from the progenitorcell. Alternatively, microcolonies can be grown directly on the surfaceof nutrient agar medium or the equivalent.

Selection for specific growth of the target cells can occur in themicrocolony growth step. For example, a test might be designed to detectanaerobic bacteria in a sample (such a test is generally required forinjectible pharmaceutical finished products, for example). In this case,the growth step could be carried out under an anaerobic atmosphere in abell jar. Selective growth media can also be used to achieve selectivemicrobial growth at this step. For detecting bacterial resistance toantibiotics, for example, cells are generally incubated in the presenceof various antibiotics at several concentrations. Resistance to acertain concentration of antibiotic is inferred if a bacterial straingrows comparably in the presence and absence of antibiotic at thatconcentration.

The invention can detect various colony morphologies. Many types ofgrowing cells form simple discrete dome-shaped colonies on commonsubstrates (nutrient agar media and membranes). Others form irregularlyshaped colonies or filamentous colonies. Furthermore, colony morphologycan depend on growth conditions (e.g., nutrients, temperature, andsubstrate). Some types of cells are mobile and do not form discretecolonies at all. If it is important to detect the growth of suchorganisms motility inhibitors can be added to the medium. Thus, growthconditions should be chosen and control experiments carried out toinsure that target cells form detectable microcolonies. If necessary,growth conditions can be modified or multiple conditions may be used inparallel tests.

Step 4: Optional Labeling of Microcolonies

In this optional step, category-binding molecules and associatedsignaling moieties (also called the probes, labels, or stains) arebrought into contact with target cells in the sample under conditionsthat facilitate specific binding. For example, an ensemble ofcategory-specific nucleic acid sequences is hybridized to complementarytarget sequences in the sample in this step. Similarly,category-specific antigens in the sample are allowed to bind to thecorresponding category-specific antibodies.

There are several possible physical configurations for the binding stepand binding can be carried out at various points in the testingprocedure. For example, target cells can be labeled in a liquid samplebefore depositing the target cells in the detection zone. Unbound probescan then be effectively removed during the depositing step or bywashing. A disadvantage of this approach is that the signal generallydoes not increase with microbial growth. Stronger signals are generallyobtained by labeling microcolonies during or after microbial growth. Thelabeling reagent can be added to the nutrient media so that the microbesare continuously exposed to the reagent during growth. Alternatively,microcolonies can be exposed to the probes after growth. For example,microcolonies on a membrane can generally be fixed and the relevantcategory-specific binding sites exposed by drying, heating, and/orexposure to chemicals (e.g., NaOH or chloroform vapor). Labeling canthen be effected by overlaying the microcolonies with the labelingreagent or by placing the membrane on a pad that has been saturated withthe reagent. Generally, a washing step is used to remove unboundreagent. The concentration of the category-binding molecules isoptimized to achieve rapid binding kinetics. The chosen conditions forselecting for specific binding depend on the characteristics of thecategory-binding molecules and their interactions with target molecules.Specific conditions and procedures are described in the examples below.

Step 5: Enumerating the Microcolonies

Enumerating the target cells in the sample occurs in the final step oftesting applications based on the invention. The enumeration step itselfcomprises the steps of imaging, image analysis, and report generation.

The invention can detect microscopic colonies with no magnification. Lowmagnification imaging facilitates the imaging of a large area which, inturn, facilitates scanning large samples. Some embodiments of theinvention detect microscopic colonies without magnification, in part, byusing high efficiency optics to direct photons emitted by themicrocolony into a small number of pixels of photodetector arrays.

The imaging method used depends on the type of signal generation chosenin step 1. For example, the imaging process is different depending onthe optical property or signaling character that is used for signalgeneration. For some signal characters (e.g., reflectance, fluorescence,light scattering, absorbance), the complexes in the detection zone mustbe illuminated by a light source. For others (e.g., chemiluminescence,thermal radiation), illumination is not required. Various detectors canbe used including electronic photodetectors, film, and directvisualization.

Detection of individual microcolonies is naturally quantitative andultra-sensitive. Quantification can be accomplished manually by countingindividual cells in a photographic or digital image or by usingautomated image analysis of digitized images. Integrating signalintensity over the sample can also be used to quantify the target cells.Signal integration is particularly useful with samples containing highconcentrations of target cells. In these cases, resolving coincidentsignals may not always be possible.

Decoding the signatures of labeled probe families allows identificationof numerous categories of target cells. An important goal of this stepis to identify the category of target cells in the sample by determiningthe signature of labeled category-binding molecules that have adhered tothe sample.

The CCD camera-based imager, shown in FIG. 3, is a useful device forlarge area imaging using when fluorescence is used as the signalcharacter. This device was used to collect the data for many of theexamples below. Excitation light may be provided by introducing lightfrom a high intensity white light source (1000 W Xenon arc lamp, ModelA-6000, Photon Technology Incorporated, Monmouth Junction, N.J.) into aliquid light-guide (5 mm core diameter, Model 380, Photon TechnologyIncorporated, Monmouth Junction, N.J.). The liquid light-guide carriesthe light to an excitation filter-wheel (BioPoint FW, LudI Electronics,Hawthorne, N.Y.) and directs the filtered beam (e.g., 9 mm or more indiameter) onto the detection surface containing the microcolonies. Theapparatus can detect microcolonies in various configurations (e.g., onthe surfaces of nutrient agar, microscope slides, coverslips, or tubesor wells with flat, optically clear, bottoms; or immobilized in nutrientagar or other substances). The incident light strikes the detectionsurface inducing fluorescence in the target cells. A portion of theemitted fluorescent light is collected by a high-collection efficiencylens system and transmitted through an emission filter-wheel (BioPointFW, Ludl Electronics) to a CCD Camera (Orca II, Hamamatsu, Bridgewater,N.J.). The design and construction of the optical train is based onprinciples and practices known to workers familiar with the art.

The invention can also incorporate other types of photodetectors andother configurations. The sensitivity of the imaging system can beincreased by choosing a more sensitive camera (e.g., a camera cooled toa lower temperature, or a camera that uses a back-thinned chip).Alternatively, the detection sensitivity and resolution can be increasedby implementing a line scanning system (e.g., BT Image Array;Hamamatsu). For line scanning, a linear CCD or photodiode array (e.g.,1×500 or 1×1000 pixels) is used to capture the image. The resolution inone dimension corresponds to the number of array elements, while thesecond dimension is generally captured by moving the sample slideperpendicularly under the linear array. Since there are fewer elements,similar sensitivity linear arrays are typically less expensive than areaformat CCD cameras.

The instrument diagrammed in FIG. 3 facilitates signal measurement frommultiple samples by using an X-Y positioning Stage (BioPoint XY, LudlElectronics) to move the sample vessel (e.g., microtiter plate) over theexcitation and collection optics. Image-Pro and Image-Pro add-inscontrol all instrument components and image acquisition. Filter wheelsare managed with the ScopePro add-in (Media Cybernetics, Baltimore Md.),and the StagePro add-in (Media Cybernetics, Baltimore Md.) handles stagepositioning, while the camera control is via the Hamamatsu Orca IIdriver (Hamamatsu, Bridgewater, N.J.). Image-Pro Plus is also used forImage-Processing and analysis as described below.

Embodiments of the invention using white light illumination utilizespectral filters to provide an optimal excitation peak for each of thefluorophores. The white light spectrum is large, allowing a wide varietyof fluorophores to be selected to eliminate emission spectrum overlaps.Typically spot sizes achievable with white light illuminators, e.g., 2mm to 5 mm, are appropriate for large area imaging. Since filter changesare relatively simple, and can be automated, white light systems arevery adaptable, allowing the same apparatus to be used for tests thatuse distinct sets of fluorophores.

The collection efficiency of the system shown in FIG. 3 is maximized byincorporating a custom designed collection optic consisting of twocomponents: a collection objective and a focusing element. Thecollection objective has high collection efficiency (≧f#/1.2) andoutputs a relatively collimated beam. The focusing lens captures thelight output from the collection objective and focuses it onto thedetection surface of the CCD. The optics are designed in two parts toallow a filter wheel to be inserted in the path of the collection lens.For certain embodiments of the invention, e.g. for some embodiments thatdo not require filter changes, it may be desirable to include a taperedoptical fiber bundle for achieving high collection efficiency. Thefiberoptic bundle contains fibers that collect light proximally to thesample and channel the light directly to a CCD chip. Alternatively, theinvention can detect signals very sensitively using direct proximaldetection in which the sample is applied directly or in close proximityto the CCD chip (for highest sensitivity to the back of a back-thinnedCCD chip).

In addition to the white-light, multi-spectral system described above,we have also developed a simpler single-color fluorescence imagingsystem for non-magnified large area imaging. In the system shown in FIG.4, excitation light is provided by a 532 nm Frequency-Doubled DiodeLaser (50 mW, Model # BWT-50E, B&W Tek, Newark, Del.). Since thisdetection uses a single color, filter wheels are not necessary. A singleexcitation filter removes harmonics from the laser output (ModelHQ532/10x, Chroma Technology, Brattleboro, Vt.) and a single emissionfilter (Model HQ620/60m, Chroma Technology, Brattleboro, Vt.) allowsonly specific fluorescent signals to pass to the CCD camera. The systemsalso use a less-expensive CCD camera (Model KX-2E, Apogee CCD, Auburn,Calif.) than the one described previously, to capture images. Theinstrument can easily be adapted to multicolor analysis by incorporatingmultiple lasers and filter sets.

The CCD cameras incorporated in the invention are generally cooled to atemperature between −5° C. and −50° C., sufficient for integration timesfrom ten seconds to about two minutes (depending on the camerasensitivity) with minimal camera noise build-up. Longer integrationtimes generally give higher sensitivity by allowing the collection ofthe photons emitted from the fluorophores for an extended period. Longintegration times are inappropriate for line scanning; however, thereare back-thinned linear arrays available that have very high quantumefficiencies, increasing sensitivity.

The invention can also use interferometer-based spectral imaging for thedetection and decoding of signals (Schrock, E., 1997, supra). Using thistechnique, light emitted or scattered by signaling moieties is splitinto two paths, passed thorough prisms (so that different wavelengthstravel different distances), and allowed to recombine to create aninterference pattern. Fourier analysis of the interference patterngenerates a spectrograph for each point in the image.

Alternatively, photographic film can be used to record images of thetarget cells inexpensively in a sample. When the signaling character ischemiluminescence, this approach is most easily implemented. Imagescollected on film can be digitized in commercial scanners for datastorage and for digital image analysis.

For embodiments of the invention that generate digital images, computersoftware identifies and quantifies the target microcolonies. For atypical assay in which different classes of fluorescent signalingmoieties are used, the software superimposes the appropriatefluorophore-specific images, identifies the target cells by determiningwhich signature or combination of signals is emitted by each targetmicrocolony, and enumerates each category of target microcolony that ispresent in the sample. The software may also: (1) correct forillumination non-uniformity; (2) correct for fluorescence cross-talkthrough a deconvolution matrix; (3) align images using registrationmarks imprinted on the substrate; (4) compare images from different timepoints; (5) apply algorithms for discerning growing microcolonies fromnon-growing objects; (6) assign an ID code to each imaged microcolony inthe sample based on comparison to a look up table; (7) record the imagedsample bar code for sample identification; and (8) automatically savethe output data, images, and bar code to a database that can be queried,e.g., via a web browser interface. Commercially available image analysispackages can be used to provide these functions. Software packages formulticolor image analysis can be used (e.g., Image-Pro, MediaCybernetics; MetaMorph, Universal Imaging; MatLab; The MathWorks).

It is useful to outline here the software packages and methods that wereused to analyze the fluorescence data collected in many of the examplesthat follow. The detection surface was imaged to determine the number offluorescent objects and/or the total fluorescent signal. Thefluorescence was captured from the membrane by a CCD camera and storedas a TIFF (Tagged Image File Format) image file that contained recordsof pixel locations and intensities. Three approaches were used toquantify the assay results. The total integrated signal of the imageddetection zone was determined by summing the fluorescent signal from allof the pixels. The integrated signal from the sample was compared tothat of negative controls. Measuring the total integrated signal isespecially useful for samples containing numerous target cells. A secondapproach was to count the fluorescent objects in the detection zone. Athird approach was to integrate the intensity of all of the pixelscontained within the fluorescent objects (as opposed to summing theintensity of all of the pixels in the image). All image analysis wasperformed using Image-Pro v 4.0 (Media Cybernetics, Silver Springs,Md.).

Obtaining the total integrated signal was achieved by initially definingan area on the membrane (the area of interest). Image-Pro allows thearea of interest to be converted into a single object and otherImage-Pro tools permit the total signal of the pixels represented inthis object to be summed. A similar image from a membrane onto which notarget cells were added was then analyzed in the same way and used as anegative control. The negative control values were subtracted from thevalues of target containing samples. This subtraction removed both assayand electronic noise.

The second and third quantification methods used Image-Pro'sobject-finding utility. This utility joins contiguous pixels that have avalue (signal) above an automatic or user-defined threshold. Thisestablishes a contour line around the perimeter of the object. Theperimeter pixels and those inside are defined as the object, and summingthese pixel values results in the object integration value. The analysissoftware was then used to count all the objects in an area of interestthat represents the bottom of the sample container and, in addition,could be used to calculate the integrated signal intensity of allobjects found.

Using the IPP Image-Pro macro language, the above utilities can beautomated to allow batch processing of several images at one time. Inaddition, the data can be manipulated with other user-defined IPPscripts. For example, objects below or above a certain size (area) orintensity can be included or excluded, which can be a useful tool fordust exclusion. Other important parameters for image analysis thatdetermine object definition (e.g., acceptance and rejection criteria)vary by application and should be optimized accordingly.

Various aspects of the invention can be automated including linking thesteps outlined above. Consider an application for analyzing liquidsamples such as pharmaceutical water for injection or a clinical urinesample. The automated system, starting with the sample in a collectionbeaker, could collect the target cells onto a membrane by filtration,place it on growth media, incubate the target cells under growthconditions, image the membrane at regular intervals, and report theresults. Alternatively, individual functions of the invention can beautomated. For example, modules for automatically loading and unloadingpetri dishes (or alternative disposables used for growing microbes) intothe imaging instrument and for automatic focusing can be incorporatedinto the system.

EXAMPLES

The examples below provide technical details for implementing variousembodiments of the invention for use in conjunction with a range ofapplications are not intended to be limiting.

Example 1 Detection and Identification of Bacterial Microcolonies UsingNon-Magnified Large Area Imaging

Background and objectives: Detection of microbial growth is at the coreof both clinical microbiology (e.g., bacterial identification andantimicrobial susceptibility testing) and industrial microbiology (e.g.,mandated sterility testing), but the commonly used methods are slow. Theconsequent delays in analysis cause needless death and suffering inclinical situations and exact a large financial cost in industry.

Using non-magnified large area imaging to detect individualmicrocolonies exploits the advantages of microbial culture whileavoiding the substantial disadvantages of traditional and emergingmethods. Advantages of in situ replication analysis using the inventionare: speed; ease of multiplexing (scanning for more than one microbe);and the ability to detect and identify without sacrificing microcolonyviability (essential for efficient antimicrobial susceptibilitytesting).

Experimental objective. The example demonstrates the invention's abilityto detect in situ replication of bacterial microcolonies. The principleof the method is diagrammed in FIG. 7. Bacteria are deposited on afilter and allowed to replicate in situ. The resulting microcolonieswere labeled in two ways: with the nucleic acid stain Syber Green I andby binding to group-specific antibodies labeled with FITC. The labeledmicrocolonies were then detected using CCD-based non-magnified largearea imaging.

Experimental methods. E. coli MG1655 cells were grown overnight in LBmedium to a density of approximately 10⁹ cells/ml. The approximatenumber of cells was determined by counting dilutions of the overnightculture in a hemocytometer. The overnight culture was then diluted toachieve about 10³ cells/ml. One milliliter of the dilution was depositedon a black polycarbonate filter (Osmonics; cat. num. K02BP04700) using avacuum filtration device and a plastic funnel cup (Millipore Microfil VUser Guide, PF07114, Rev A 3/00). Sixteen separate filters with ˜1000cells were prepared in this manner, four filters for each of four timepoints (0, 1.5, 3 and 24 hours). After filtration, each filter wasplaced on a separate agar plate containing LB growth medium, which waspre-warmed to 37° C., and placed in a 37° C. incubator. Periodically (0,1.5, 3, 24 hours) four filters were removed from the incubator. Two ofthese filters were fixed in 3.0% formaldehyde for 10 minutes, by addingthe filter bacteria side up on top of a 500 μl spot of formaldehydewhich was spotted on a piece of Parafim™. After fixation the filterswere put on an absorbent pad to soak up the excess formaldehyde. Next a10× solution of Syber Green I (200 μl, Molecular Probes) was added ontop of the filter. The cells were allowed to stain for 10 minutes. Theother two filters not used in the nucleic acid staining were blockedwith PBS-B for 15 min and then FITC labeled anti-E. coli antibodies(Fitzgerald) were added to the filters. After 30 minutes of incubation,the filters were placed on an absorbent pad to soak up any residualliquid. All membranes were then imaged by placing the filter on aCCD-base imager (described in Step 5 of Detailed description section andshown in FIG. 3) so that the bacteria were facing the illuminationsource and CCD chip.

Results. In this example single cells were allowed to go through severalreplicative generations in order to from microcolonies. Themicrocolonies were labeled with either Syber Green I or a FITC-labeledantibody. In FIG. 6 the upper row of panels shows the 0 hour time pointcontaining single cells. The lower row of panels shows microcoloniesafter 3 hours of incubation. There was a substantial increase in sizeand signal of the objects detected by CCD imaging over time due to theincrease in the number of cells at the sites where the colony-formingcells were originally deposited. The detection of growth is central tomedical and industrial microbiological practice. This example shows thatthe invention can dramatically decrease the time required for detectionof microbial growth.

Example 2 Autofluorescence-Based Detection of Bacterial MicrocoloniesUsing Non-Magnified Large Area Imaging

Background and objectives: The importance of methods that detectmicrobial growth and the limitations of current methods are discussed inthe Background section. This example demonstrates a very simple yetpowerful method based on the present invention that rapidly detects thegrowth of bacterial microcolonies. The method relies on the intrinsicfluorescence (autofluorescence) of the target cells for generatingdetectable signal. Thus, this method does not use category-bindingmolecules or exogenous signaling moieties to achieve non-magnified largearea imaging of microscopic target cells. The advantages of reagent-lessnon-destructive enumeration include generation of purified cultures (formicrobial identification and antibiotic susceptibility testing, improvedmethod validation, and the ability to follow microbial growth over time(for object discrimination and growth kinetics).

Experimental methods. E. coli MG1655 cells were grown as in Example 1.Bacterial cells were diluted serially (ten-fold dilutions) with sterilePBS. Bacterial cells (50 ml volume of the 10⁻⁷ dilution) were depositedon a black polycarbonate filter (Osmonics; cat. num. K02BP04700) using avacuum filtration device and a plastic funnel cup (Millipore Microfil VUser Guide, PF07114, Rev A 3/00). A negative control was prepared byfiltering sterile PBS. After filtration, each filter was placed on aseparate agar plate containing LB growth medium, which was pre-warmed to37° C., and placed in a 37° C. incubator. The viable cell count of the10⁻⁷ dilution was determined by filtering replicate samples andincubating the filter on LB agar. This process indicated that the 10⁻⁷dilution contains approximately 1000 cells per 50 ml. At 5.25 hours,membranes were imaged by placing the filters held on glass microscopeslides into a CCD-based imager (described in Step 5 of Detaileddescription section and shown in FIG. 3) so that the bacteria werefacing the illumination source and CCD camera. A FITC optical filter set(Chroma; excitation 470/40 nm, emission 522/40 nm) was used and a onesecond exposure captured using software control (Image Pro Plus, version4.1; Media cybernetics).

Results. FIG. 7 shows the autofluorescence-based detection of bacterialmicrocolonies after 5.25 hours of growth. A filter containingmicrocolonies provided strong signals after a one second exposure (FIG.7, left panel), while a filter lacking microcolonies, but that wasotherwise identically processed and imaged, did not exhibit such signals(FIG. 7, right panel).

The example demonstrates that this very simple embodiment of theinvention is a powerful approach for microbial growth detection. Thetechnique could be used to make many important microbial diagnosticsapplications more efficient including sterility testing, environmentaland water testing, microbial identification, and microbialsusceptibility.

Example 3 A Simple Method for Validating a Rapid Reagent-Less MicrobialEnumeration Test Using an Internal Comparison to the Traditional CultureMethod

Background and objectives: Proving the equivalence of a newmicrobiological test to the “gold standard” method is an essential taskfor both the developers of new methods and their customers. Formalizedvalidation requirements are generally codified in governmentalregulations that guide the introduction of new microbiological methodsin industry and healthcare. New methods for microbiological testing inthe pharmaceutical industry have sometimes floundered because of thedifficulty of proving equivalence to the accepted methods. The goal ofthis example is to demonstrate a simple method for proving theequivalence of a test based on the invention to the traditionalmicrobial culture test.

Experimental methods. E. coli MG1655 cells were grown and analyzed as inExample 2. After imaging the microcolonies, the filter was re-incubatedat 37° C. for about 15 hrs. The resulting macrocolonies were imagedusing reflected white light supplied by an incandescent microscope lampshining obliquely on the plate. Otherwise, the same imaging system wasused to collect the reflected light as was used to detect microcolonyautofluorescence.

Results. That this embodiment of the invention does not harm themicrobes is apparent by comparing the left and right panels of FIG. 8.The exact correspondence between the “ancestral” microcolonies (leftpanel) and their “descendant” macrocolonies (the right panel) by thisinternal comparison should facilitate demonstration of equivalence tothe traditional microbial culture enumeration test.

Example 4 Accuracy and Limit of Detection of Autofluorescent MicrocolonyDetection Using Non-Magnified Large Area Imaging

Background and objectives: Accurate detection of small numbers ofmicrobes is critical in both healthcare and industrial microbiology. Forexample, only one bacterial cell in a 10 ml blood sample may be presentin a patient with a potentially fatal blood infection. Similarly,sterility testing of an injectible drug in pharmaceutical manufacturingmust detect a single living microbial cell in a sample. In both cases,false negative results and false positive results can have severeconsequences. The fraction of test results that are false positives andfalse negatives defines the accuracy of a test method.

The goal of this example is to show the accuracy of the invention at thelowest level of target cells.

Experimental methods. E. coli MG1655 cells were grown and analyzed as inExample 3. However, for this example a dilution of cells was applied tomultiple filters (n=101) so that on average the detection zone on aboutone in five filters was expected to contain a single target cell. After5 hr of incubation each filter was imaged and scored for the presence ofmicrocolonies. The filters were then re-incubated overnight and scoredfor the presence of macrocolonies. The results obtained using theinvention were then compared to the results obtained using traditionalvisual method.

Results. FIG. 9 shows the method used in this example to measure theaccuracy of the invention when the samples contain the extremely lowlevels of target cells. For each of the 101 filters, the result obtainedby scoring the microcolonies was the same as the result obtained by thetraditional method. As judged by either the presence of microcolonies ormacrocolonies, most filters (n=80) had no deposited target cells.Furthermore, the filters containing deposited target cells (n=21), themicrocolonies occurred in the same numbers (several filters had multipletarget cells, as would be expected statistically) and with the identicalplacement on the filters as did the macrocolonies, adding furtherrobustness to the results. The invention and the “gold standard” methodwere in 100% agreement, with no false positives or false negativesdetected. Thus, the results indicate that the invention is accurate atvery low target cell levels.

Example 5 Determining the Number of Microbial Cells in AutofluorescentBacterial Microcolonies Rapidly Detected Using Reagent-LessNon-Magnified Imaging

Background and objectives: The goal of this example is to demonstratethe sensitivity and speed of reagent-less detection of microcolonyautofluorescence using large area imaging. Rapid detection of microbialgrowth is the result of the invention's ability to detect microcoloniesat early stages when the number of cells is small. The experiments inthe example determine number of bacterial cells in microcoloniesdetected by non-magnified CCD imaging.

Experimental methods: A single colony of freshly grown Escherichia coli(ATCC, Cat. No 8739) was inoculated into a conical tube (50 ml)containing growth medium (TSB; 10 ml) and incubated (16 hours, 37° C.,150 rpm). This culture containing stationary phase cells (2.4×10⁹/ml)was used to inoculate an Erlenmeyer flask (500 ml) containing pre-warmedTSB (37° C., 100 ml) to produce a log phase culture for optimal time todetection. This flask, containing pre-warmed TSB was inoculated with thestationary phase culture (100 μl) and incubated 2 hours, 37° C., 150rpm). A culture established in this way was found to contain ˜5×10⁷bacteria/ml via pour plate titration. The log phase culture was dilutedin PBS (10⁻⁶). A volume (10 ml) of this dilution was filtered through amembrane (Chemunex Inc., Chemfilter CB0.4, Black, PET-23, 25 mm)supported over an absorbent pad (Whatman Inc., Cat. No. 1441325) using afiltration device (Millipore Inc., 1225 Sampling Manifold, Cat. No. XX27025 50). After the bacteria were collected on the membrane, the membranewas placed on a pre-warmed TSA plate (32.5° C.). An image of the platewas captured (30 sec exposure) using non-magnified large area imagingwith a FITC optical filter set (Chroma; excitation 470/40 nm, emission522/40 nm) using software control (Image Pro Plus, Media Cybernetics).Following this initial image capture the plate was placed in anincubator (32.5° C.) for growth. The plate was removed from theincubator after 2.5 hours of growth and the same field was imaged againusing the image capture settings applied previously. Following imagecapture, the membrane was immediately fixed (1.5% formaldehyde infiltered type 1 water for 5 minutes) followed by two washes (PBS, 5 mineach) by placing the membrane on 3M Whatman paper impregnated witheither fix or wash solutions as indicated. The membrane was placed onthe sampling manifold with all but one placement blocked off with astopper. Vacuum filtration was applied for 15 seconds. To stain themembrane in order to enumerate the bacteria in an individualmicrocolony, propidium iodide (1 ml, 5 μg/ml) was added to the wall ofthe cup while vacuum pressure was applied, followed by type 1 water (1ml). Vacuum pressure was applied for an additional 15 seconds afterwhich the membrane was removed and placed on a glass slide, dried, andmounted with a coverslip using the Pro Long Antifade Reagent (MolecularProbes, Eugene, Oreg., Cat. No. P-4781). The stained microcolonies wereimaged using fluorescence microscopy (Axioplan II fluorescentmicroscope, Carl, Zeiss Inc., Thornwood, N.Y.; Cy3.5 filter set, ChromaId. No. SP-103, excitation 581/10 nm, emission 617/40 nm, 400×) fittedwith the SPOT RT camera (Diagnostic Instruments, Sterling Heights,Mich., Model No. 2.3.1, 2 seconds, red spectra only selected) followingspatial registration of these with their corresponding unstainedmicrocolonies identified using large area imaging.

Results: FIG. 10 shows the results obtained in this example. Threemicrocolonies detected after 2.5 hr of growth at 32.5° C. using theinvention were stained and analyzed using high power microscopy. Thethree microcolonies contained 45, 48, and 50 cells, respectively. Nosingle bacterial cells were observed near the microcoloniesdemonstrating that the microcolonies remained intact throughout thestaining procedure. Note that visible colonies of E. Coli (˜1 mmdiameter) contain approximately one million times this number of cells.Thus, using non-magnified reagent-less detection, the invention candetect microcolonies after only a few generations of cell division.

Example 6 CCD-Based, Non-Magnified, Large Area Imaging Detection andIdentification of Bacterial Microcolonies in an Environmental WaterSample

Background and objectives: This example aims to show the power of theinvention for rapid detection of microbial growth when applied to avariety of anonymous environmental microbes that are likely to benutritionally stressed.

Water is a common ingredient in the production, processing, andformulation of many pharmaceuticals. Detection of bacteria inpharmaceutical water is a fundamental step in the manufacturing processbecause the bacteria themselves or their metabolic products could causeadverse consequences if is present in the final product. Proliferationof bacteria may occur in the production, storage, and distribution ofthis substance. Drinking water is the source feed water forpharmaceutical water and it is the major exogenous source of microbialcontamination. Fecal coliforms and a wide variety of microorganisms,largely Gram-negative bacteria, may be present in drinking water. Thecommonly used methods to detect bacteria in water are slow and thushamper timely system control.

Using non-magnified large area imaging to detect individual bacterialmicrocolonies exploits the advantages of in vitro replication analysiswhile avoiding the substantial disadvantages of traditional and emergingmethods. Advantages of in situ replication analysis using the inventionare: speed and the ability to detect and identify without sacrificingmicrocolony viability (useful for identifying the source of microbialcontamination in a product or process or determining whether aparticular microorganism is harmful to the products or processes inwhich the water is used.)

Experimental overview. The example demonstrates the invention's abilityto detect in situ replication of bacterial microcolonies before thesecolonies grow into macrocolonies. Bacteria are deposited on a filter andallowed to replicate in situ. The resulting microcolonies andmacrocolonies were detected using CCD-based, non-magnified, large areaimaging using autofluorescence (FITC excitation and emission filters)and reflectance of white light. Experimental methods. Water wasaseptically collected from the Charles River (Cambridge, Mass.) and usedin the experiment within one hour of collection. The Charles River waterwas centrifuged at a setting of 14,000 rpm in an Eppendorf Centrifuge5415C for 1-2 seconds. The centrifuged Charles River water was diluted1:10 with sterile Type I water and 1.0 ml of this was deposited on ablack, mixed cellulose ester filter (Millipore; cat. num. HABP04700)using a vacuum filtration device and a sterile plastic funnel (MilliporeMicrofil® 100 ml Funnel, cat. num. MIHABG072). Each filter afterfiltration was placed on a separate agar plate containing R2A growthmedium (Becton Dickinson/Difco; cat. num. 218263). Ten separate filterswere prepared and the agar plates were incubated at 32.5° C. for up to74 hours. Periodically (after 17, 24, 42, 50, 68, and 74 hours) the agarplates were removed from the incubator and the filters were imaged byplacing the plates on a CCD-based imager so that the bacterial colonieswere facing the illumination source and CCD chip. The illuminationsource for reflectance was provided by a Fiber-Litee Model 190Convection Cooled 30 Watt Quartz Halogen Illuminator (Dolan-JennerIndustries, Inc., Lawrence, Mass.), and the illumination was directed atan oblique angle onto the filter. The naked eye is capable of seeingbacterial colonies that are 0.5 mm or greater in diameter, so this sizecriterion was used as a discriminating characteristic of a bacterialcolony. The colonies that were 0.55 mm or greater in diameter wereidentified and counted in the reflectance images. When autofluorescentmicrocolonies that gave rise to a macrocolonies could be detected wasalso determined. Autofluorescent images were analyzed to determine whenthe progenitors of 74 hr macrocolonies appeared. At various time pointsthe percentage the 74 hr macrocolonies that were detectable asautofluorescent microcolonies was plotted.

Results. In this example bacterial cells from a water sample wereallowed to replicate in order to form microcolonies and macrocolonies.Both types of colonies were detected by using the invention andidentified by autofluorescence and reflectance. The data shown in FIG.11 indicates that the number of colonies that can be visually observedincreased from 11% (6 colonies) at 17 hr to 100% (53 colonies) at 74 hr.Ninety-four percent (50 colonies) of the macrocolonies detected at 74hours were detected as autofluorescent microcolonies at 24 hours. Thisexample shows that the invention can dramatically decrease the timerequired for detection of bacterial growth and thus decrease the amountof time needed for a bacterial test for water.

Example 7 Correlation Between CCD-Based, Non-Magnified, Large AreaImaging Detection of Bacterial Microcolonies and a Traditional Methodfor Enumerating Bacteria

Background and objectives: The goal of this example is to determine thenumerical correlation of the results obtained using the presentinvention to detect microcolonies rapidly and those obtained usingslower traditional microbial culture.

Experimental objective: The example compares the enumeration ofmicrocolonies by the invention and the classical “pour plate” culturemethod. Bacteria were deposited on a filter and allowed to replicate insitu. The resulting microcolonies were detected using CCD-based,non-magnified, large area imaging using autofluorescence (FITCexcitation and emission filters). The number of microcolonies obtainedwith the invention was then compared to the number of macrocolonies thatwere obtained with the pour plate method.

Experimental methods: E. coli 8739 cells were grown overnight in TSB toa density of approximately 10⁹ cells/ml. Ten fold serial dilutionsstarting with approximately 10⁷ cells/ml and ending with approximately10² cells/ml of the overnight culture were made in PBS. An aliquot fromeach serial dilution was further diluted with PBS such that 1.0 ml wouldcontain approximately 50 bacteria. One milliliter was placed in a petridish together with 35 ml of melted (47° C.) Tryptic Soy Agar (TSA)(Becton Dickinson/Difco; cat. num. 236950). The agar plates were allowedto cool at room temperature and then the plates were incubated overnightat 32.5° C. Ten agar plates were prepared for each serial dilution.Macrocolonies in the agar plates were counted by visually inspecting theplates. Dilutions of bacteria (11.3 ml) were deposited on a black mixedcellulose ester filter (Millipore; cat. num. HABP04700) using a vacuumfiltration device and a sterile plastic funnel (Millipore Microfil® 100ml Funnel, cat. num. MIHABG072). Each filter was placed on a separateagar plate containing TSA. Ten separate filters were prepared for eachserial dilution, and the agar plates were incubated at 32.5° C. for 7hours. The plates were then removed from the incubator, and the filterswere imaged by placing the plates on a CCD-based imager so that thebacterial colonies were facing the illumination source and CCD chip.Autofluorescence from each microcolony was detected using FITCexcitation and emission filters. Eleven times more volume was used withthe filter because each image constitutes approximately 1/11^(th) of theentire filter surface. Thus, each image should contain approximately thesame number of bacteria as was put into each pour plate. The number ofmicrocolonies in each image was determined by visually inspecting theimage. The number of bacteria in each serial dilution was calculated bymultiplying the number of microcolonies or macrocolonies by a dilutionfactor.

Results: In this example bacterial cells were allowed to replicate andform microcolonies on a filter or macrocolonies in agar plates. Themicrocolonies were detected using the invention, and the macrocolonieswere detected using a classical culture method and visually inspectingthe agar plates. The concentration of bacteria as determined by eachmethod for each serial dilution was plotted, and the results are shownin FIG. 12. Each point represents the average of ten separatedeterminations. A positive correlation was obtained between the resultsobtained with the invention and the results obtained with the classicalpour plate method. The correlation coefficient of 0.9996 indicates astrong linear relationship between counting microcolonies with theinvention and macrocolonies with a classical culture method.

Example 8 Dynamic Range and Linearity of a Reagent-Less Enumeration Test

Background and objectives: Two of the validation criteria for a newmicrobiological testing method are the range and linearity of the newmethod. The range is the interval between the upper and lower levels ofmicroorganisms that have been demonstrated to be determined withprecision, accuracy, and linearity using the new testing method. Thelinearity of a microbiological test method is its ability to elicitresults which are proportional to the concentration of microorganismspresent in the sample within a given range. The example demonstrates theinvention's linearity over a range of bacterial levels. The inventiondetects the presence of microcolonies on the surface of a filter andquantifies the autofluorescent signal of the microcolonies by usingCCD-based, non-magnified, large area imaging.

Experimental methods. E. coli 8739 cells were grown overnight in TSB toa density of approximately 10⁹ cells/ml. Ten fold serial dilutionsstarting with a 10⁻⁴ dilution of the overnight culture and ending with a10⁻⁹ dilution were made in PBS. Five ml of each serial dilution wasdeposited onto a black, mixed cellulose ester filter (Pall GelmanLaboratory; cat. num. 66585) using a vacuum filtration device and asterile plastic funnel (Millipore Microfil 100 ml Funnel, cat. num.MIHABG072). Each filter after filtration was placed on a separate agarplate containing Trypticase Soy Agar with Lecithin and Polysorbate 80(Becton Dickinson BBL, cat. num. 211764). One filter was prepared foreach serial dilution and then the agar plates were incubated at 32.5° C.for 6.5 hours followed by an overnight incubation at 32.5° C. At the 6.5hour time point, the agar plates were removed from the incubator, andthe filters were imaged by placing the plates on a CCD-based imager sothat the bacterial colonies were facing the illumination source and CCDchip. Autofluorescence from each microcolony was detected using GFPexcitation and GFP-LP emission filters. The autofluorescent signal fromthe microcolonies in each image was quantified using ImagePro software(Media Cybernetics, Inc., Version 4.5.0.19). Following the overnightincubation, the agar plates were inspected visually, and the number ofmacrocolonies present on the filters prepared with the 10⁻⁸ and 10⁻⁹dilutions was counted. The number of macrocolonies on these two filterswas used to calculate the number of bacteria added to each membrane andthe concentration of bacteria in the initial overnight culture.

Results. In this example the bacterial cells were allowed to replicateand form microcolonies on a filter in agar plates. The microcolonieswere detected by using the invention and identified by GFP-LPautofluorescence. The autofluorescent signal from the microcolonies ineach image was quantified using ImagePro software. The autofluorescentsignal in each image was plotted versus the number of bacteria added toeach filter and the results are shown in FIG. 13. The data is linearover a 5 log range of bacteria levels. This range is significant becausethe range of some classical culture methods i.e. pour plates is only 2logs. The results also show very strong linearity with an R² value of0.9929. This value is within the acceptable R² values (0.8 to 1.2) for anew microbiological testing method (Evaluation, Validation, andImplementation of New Microbiological Testing Methods. 2000; PDA Journalof Pharmaceutical Science & Technology 54 (Supplement TR33), 1-41).

Example 9 Rapid Antimicrobial Preservative Effectiveness Testing withoutSample Dilutions

Background and objectives: Antimicrobial preservatives are added toarticles packaged in multidose containers to protect against growth ofmicroorganisms that may be introduced by the manufacturing process or bycustomers during withdrawal of individual doses. Antimicrobialeffectiveness must be demonstrated for pharmaceutical products thatcontain intrinsic antimicrobial activity or products that contain anantimicrobial preservative. The tests are very laborious and expensiveto perform because of the large number of sample dilutions that must beanalyzed. Typically an antimicrobial preservative effectiveness testrequires analysis of hundreds of microbial culture plates. An importantgoal of this example is to demonstrate the potential of the invention toeliminate most of the labor of the test by obviating the need for sampledilutions.

Experimental methods. E. coli 8739 cells were grown overnight in TSB toa density of approximately 10⁹ cells/ml. Bacteria (8.48×10⁶ total or2.12×10⁵ cells/ml)) were added to 40 ml of sterile PBS or 40 ml of OscoBrand Sterile Preserved Saline Solution (Distributed by AmericanProcurement and Logistics Company, Lot num. 1T016, Exp. June 3). Thesetwo solutions were incubated at room temperature for 168 hours. After 0,24, 96 and 168 hours, 5 ml of the PBS containing bacteria and 5 ml ofthe Osco Saline containing bacteria were removed and added to separatetubes containing 45 ml of sterile D/E Neutralizing Broth (BectonDickinson/Difco, cat num. 281910). The diluted sample was then depositedonto a black, mixed cellulose ester filter (Pall Gelman Laboratory; cat.num. 66585) using a vacuum filtration device and a sterile plasticfunnel (Millipore Microfil® 100 ml Funnel, cat. num. MIHABG072). Eachfilter was placed on a separate agar plate containing Trypticase SoyAgar with Lecithin and Polysorbate 80 (Becton Dickinson BBL, cat. num.211764). One filter was prepared for each solution at each time point.The agar plates were incubated at 32.5° C. for 6.5 hours. The agarplates were removed from the incubator, and the filters were imaged byplacing the plates on a CCD-based imager so that the bacterial colonieswere facing the illumination source and CCD chip. Autofluorescence wasdetected using GFP excitation and GFP-LP emission filters. Theautofluorescent signal from the microcolonies in each image wasquantified using ImagePro software (Media Cybernetics, Inc., Version4.5.0.19). Using the standard curve shown in Example 8, theautofluorescent signal obtained by the ImagePro analysis was convertedinto the number of bacteria added per membrane and then theconcentration of bacteria per ml of solution (PBS or Osco Saline) foreach time point. Given the starting concentration of bacteria after 0hours of incubation, the log decrease in bacterial concentration wascalculated for the 24, 96, and 168 hour time points. After 0, 24, 96,and 168 hours, 100 μl was removed from the PBS and Osco Saline solutionscontaining bacteria and added to 900 μl of D/E Neutralizing Broth (1:10dilution). Serial 10 fold dilutions in 1.0 ml sterile PBS were then madeof the 1:10 dilution starting at 10⁻¹ and ending at 10⁻⁶. The entirevolume of the 10⁻¹ through 10⁻⁶ dilutions was added to 30 ml of melted(45° C.) Trypticase Soy Agar with Lecithin and Polysorbate 80. The agarplates were allowed to cool at room temperature and then the plates wereincubated overnight at 32.5° C. Bacterial colonies were visually countedin the plates of the two lowest dilutions which contained less than 300colonies per plate. These numbers (multiplied by the appropriatedilution factor) were used to calculate the concentration of bacteria inthe PBS and Osco Saline solutions. Given the starting concentration ofbacteria after 0 hours of incubation, the log decrease in bacterialconcentration was calculated for the 24, 96, and 168 hour time points.The log decrease in bacterial concentration as determined by theinvention was plotted versus the log decrease in bacterial concentrationas determined by the pour plate method (a classical, growth-based,microbiological enumeration method). The results are shown graphicallyin FIG. 14.

Results. The results in FIG. 14 show that the antimicrobial preservativein the Osco Saline solution (0.1% Sorbic Acid) is effective indecreasing the concentration of bacteria. No decrease in bacterialconcentration was observed in the PBS. The data indicate a linearcorrelation (R²=0.9633) between the two enumeration methods even thoughno dilutions were required by the invention. The results show thepotential of the invention to save most of labor and materials byeliminating the onerous sample dilutions of the traditional method.

Example 10 Autofluorescence-Based Detection of a Heat-StressedBiological Indicator Using Non-Magnified Large Area Imaging

Background and objectives: The goals of this example are to show thepotential application of the invention for applications that usethermo-resistant spores as biological indicators. One importantapplication is sterilizer quantification methods for insuring theeffectiveness of sterilization procedures in pharmaceutical and medicaldevice manufacture and in clinical laboratories.

A further goal is to show the potential of the invention for simplifyingbiological indicator enumeration by lowering the number of requiredsamples. In the traditional pour plate method, serial ten fold dilutionscovering the entire possible range are necessary to quantify samplesaccurately. In this example, non-magnified large area imaging of theautofluorescence of the biological indicator Geobacillusstearothermophilus is used to quantify the viable spore concentration.The quantification is linear for about 3 orders of magnitude, decreasingthe number of dilutions necessary to determine the number of viablespores remaining after heat stress accurately. An autofluorescent imageis taken after a short period of growth, which is then analyzed to givean estimate of the initial concentration of viable heat-stressed G.stearothermophilus spores.

Experimental Methods Spores of G. stearothermophilus ATCC 7953 (RavenBiological Laboratories, Inc.) were diluted to a concentration of ˜2×10⁵spores/ml in sterile water and subjected to a variety of heat stressesranging from 5 minutes at 110° C. to 15 minutes at 121° C. The heattreated spores and an untreated control were serially diluted by 10-foldin water up to a 1/1000 dilution. For comparison, each sample wasanalyzed by the traditional pour plate method in addition tonon-magnified large area imaging of autofluorescence. Pour plates wereprepared by placing 1 ml of each dilution (including the undilutedstock) of each sample in a petri dish followed by the addition of 20 mlof molten Trypticase Soy Agar (TSA, BD catalogue no. 236950). Aftersolidifying, the plates were incubated at 55° C. for 48 hours andcounted manually. Plates that had between 30 and 300 colonies were usedto calculate the spore titer, unless no plates had more than 30colonies, in which case the plate containing 1 ml of undiluted stock wasused.

To prepare microcolonies for large area imaging, 1 ml of the undilutedstock and 1 ml of the 1/100 dilution were mixed with 15 ml sterile waterand filtered through a black HABP filter (Millipore catalogue no.HABP04700) using vacuum filtration and a plastic funnel cup (MilliporeMicrofil V User Guide, PF07114, Rev A 3/00). After filtration, eachfilter was placed on a separate plate of TSA. Images were taken at t=0hours using the non-magnifying CCD-based imager (described in Step 5 ofDetailed description section and shown in FIG. 3). Autofluorescence wascaptured using the FITC optical filter set (Chroma; excitation 470/40nm, emission 522/40 nm) with 5 second exposures. Plates were incubatedat 55° C., and images were taken at 8 and 20 hours. Images were analyzedusing Image Pro Plus software (version 4.1; Media cybernetics). The t=0exposures were used to find dust and other fluorescent contaminants thatmay have been on the plates prior to growth. For each image, the sum ofthe pixel intensities of all objects (where objects are defined in thisparticular example as containing pixel intensities from 3200-65301intensity units) minus the signal from contaminants at t=0 were comparedto a standard curve generated using unstressed spores, and the sporetiter was calculated from the standard curve. The value from either theundiluted sample or the 1/100 dilution was used according to which onefell within the linear range of the standard curve. The calculatedvalues of spores/ml from autofluorescence from non-magnified imagingwere compared to the values calculated from the pour plate method.

Results: A plot of the heat-stressed spore titer calculated from pourplates vs. spore titer using autofluorescent large area imaging can beseen in FIG. 15. There is a good correlation between values from bothmethods, but four pour plates were necessary for each twoautofluorescent images. In addition, pour plates take 48-72 hours toread, while the autofluorescent images can be taken and analyzed at 8-20hours of growth.

Variations. Non-magnified large area imaging of autofluorescence couldalso be used to quantify viable cell concentrations of other biologicalindicator organisms, such as Bacillus subtilis and Clostridiumsporogenes.

A variety of analyses of the autofluorescent images can be used toquantify cell concentrations. For example, object counts ofmicrocolonies can be used instead of the sum of pixel intensities of theobjects. Since the objects (microcolonies) are much smaller than fullgrown macrocolonies (that can be counted by eye), more can fit into thesame area without sacrificing the accuracy that can be lost due toobject overlap. In addition, more sophisticated object findingalgorithms can be applied to the images to deal with local fluorescentbackground, touching objects, and presence of contaminating fluorescentparticles.

Example 11 Autofluorescence-Based Detection of Bacterial Microcoloniesin Ground Beef

Background and objectives: This example illustrates the ability of theinvention to reduce the time to detection of bacterial microcolonies inground beef compared to compendial methods. Determination of totalviable bacteria count in raw meat is essential for preventing early foodspoilage. Current methods take two days, often requiring producers toship the meat before getting test results. Reducing the time todetection of microbes could prevent foodborne disease incidents,manufacturing inefficiencies, and expensive recalls.

Experimental Methods Lean ground beef (25 g) was diluted in 225 ml of0.1% peptone water and processed in a Stomacher to homogenize thesample. This sample was then diluted serially in 0.1% peptone water.Appropriate volumes of the 10⁻², 10⁻³, 10⁻⁴ and 10⁻⁵ dilutions wereadded to PBS and then poured onto two filter membrane types (MilliporeHABP Cat. No. HABP04700 0.45 μm and Chemunex CB0.4 0.4 μm Ref. no.200-C20010-01) using vacuum filtration devices. Replicate samples weremade for each dilution and filter type and incubated on TSA plates at35° C. for 48 hrs. Images were captured using a CCD-based imager at 0,6, 16, 24, and 48 hrs. A FITC optical filter set (Chroma; excitation470/40 nm, emission 522/40 nm) was used and a 10 second image wascaptured under HDyn resolution using software control (Image Pro Plus).Images were also captured with white light reflectance for 10 seconds.

Results: Data was collected from the 10⁻⁴ and 10⁻⁵ dilutions on bothmembrane filter types. The data was analyzed by counting macrocoloniesat 48 hours that were 0.5 mm in diameter or larger in reflectanceimages. These macrocolonies (≧0.5 mm) were then traced back to the 24,16, and 6 hr time points, in reflectance and autofluorescent images.FIG. 16 shows the detection times of autofluorescent microcolonies andmacrocolonies. Tracking the appearance over time of microcolonies thatgave rise to the 48 hr macrocolonies showed that 100% of themacrocolonies were detected by the invention by 16 hrs. These resultsshow the potential of the invention to reduce significantly the timerequired to achieve results compared to traditional methods.

Variations. The test in this example can be extended to test a varietyof foods, including other meats, vegetables, beverages, and dairyproducts.

Example 12 Detection of Bacteria in a Complex Sample with Non-SpecificMagnetic Selection Followed by Microcolony Detection Using Non-MagnifiedLarge Area Imaging

Objective: This example demonstrates an immunoassay method for selectingindividual bacterial cells, non-specifically, from a complicated samplefollowed by rapid detection of growing microcolonies using non-magnifiedlarge area imaging. More specifically this example demonstrates theability to select a range of bacteria efficiently from blood and thendetect the growth of the bacteria using the growth direct method. Thisexample shows that magnetic beads coated with a mixture of bindingagents, can select divergent species of bacteria from a complex sample.

Experimental Methods: FIG. 17 shows the process this example follows todetect bacteria in a complex sample. First, bacterial cells and magneticbeads are added to the sample and incubated. The magnetic beads arebound to the bacterial cells; then the complexes are sequestered usingmagnetic force. The magnetic beads are resuspended (PBS), filtered, andplated on growth media. The resulting magnetic selection supernatant isalso plated. After an incubation period, the filter is imaged at varioustime points using non-magnified large area imaging to detectmicrocolonies.

An array of magnetic particles were made by coupling magnetic particleswith active tosyl-groups (Dynal, Oslo, Norway, cat. no. 140.03) toseveral non-specific as well as specific binding agents. The agentsinclude polymyxin B sulfate (Sigma; cat. no. P1004), polymyxin Bnanoprotein (Sigma; cat. no. P2076), endotoxin neutralizing protein(Seikagaku America: naturally derived and recombinant versions, cat. no.910140-1, 910130-1 and 910120-1), endotoxin inhibitor protein (Bachem;cat. no. H-1382), endotoxin substrate (Bachem; cat. no. L-1195),anti-lipotechoic acid antibody (QED; cat. no. 15711), anti-endotoxinantibody (QED cat. no. 15306 and 15301). The coated magnetic particles(1×10⁸ per 10 μl) were sonicated (1 min; setting 8; Fisher Scientific550 Sonic Dismembrator). Combinations of the coated magnetic beads werethen added to 1.5 ml tubes of blood (1 ml, Biochemed; Human blood,sodium citrate as anticoagulant, cat. no. 10762WB) spiked withapproximately 1, 10 or 100 cells of Staphylococcus aureus (ATCC #27694). The blood, bacteria and magnets were allowed to incubate (1 hourat room temp). After incubation the beads were magnetically selectedusing a magnetic separation device (Polysciences, Inc., Warrington, Pa.,Cat. No. 8 MB4111S) to capture and secure the magnetic particles. Theblood was then decanted and plated on TSA (Difco, cat. no. 236950) aswas the initial Staphylococcus aureus inoculums of 1, 10 and 100 cells(used as controls). The magnetic particles were resuspended (1 ml PBS)and the resulting liquid containing magnetic particles-bacterialcomplexes was filtered onto a membrane (Osmonics, poretics 47 mm, 0.22μm pore, polycarbonate black filter, cat. no. 1213889), and the membranewas then placed on a TSA plate. At both the zero time point and after ashort incubation period, the filters were imaged using non-magnifiedlarge area imaging to detect the autofluorescent microcolonies. Thepercent recovery was determined by comparing the inoculum count with themagnetic capture count and using the formula: (average magneticcapture/average inoculum count) X 100.

Results: FIG. 18 shows the experimental results demonstratingmicrocolony detection after the magnetic separation. This figure showstwo images, taken at after zero and six hours of growth. The six hourimage has putative microcolonies—these are bright spots that are notseen in the zero image. To confirm that these are indeed growingmicrobial microcolonies, the filters were allowed to incubate overnightand re-imaged. Macrocolonies were detected at the positions of theputative microcolonies confirming the rapid result. Greater than 90percent recovery of Staphylococcus aureus was achieved for the 1 cellsamples. The 10 and 100 cell sample had greater than 50 percentrecovery.

Variations (broad binding agents): Numerous broadly reactive bindingagents could be used including wheat germ agglutinin,anti-enterobacterial common antigen, anti-protein A, anti-protein G, LPSbinding protein, mucin (bacterial binding agent), CD14 (binds both LPSand LPS bacterial complexes), collecting (these bind bacteria duringphagocytosis or during the complement cascade), subunits of complementitself such as C3b and C4b, human scavenger receptors (cell receptorsthat bind bacterial components) and tectonics (carbohydrate bindingproteins).

Variations (specific binding agents): A variety of types ofcategory-binding molecules, including antibodies, aptamers, and ligands,can be used to specifically select a range of cells types from complexsamples. In this example variation, selection of an E. coli O157:H7 isachieved using an E. coli O157:H7 specific antibody.

Variation of Experimental Method: In this variation, detection ofbacteria in a complex sample is achieved with analyte-specific magneticselection. The selection is followed by microcolony detection usingnon-magnified large area imaging. FIG. 17 shows the method to use forthis example. A sample containing E. coli O157:H7 is mixed with magneticparticles. The sample is then magnetically selected, filtered and imagedat a series of time points using non-magnified large area imaging.Anti-E. coli O157:H7 magnetic particles are made by couplingtosyl-modified magnetic particles (Dynal, Oslo, Norway, cat. No. 140.03;coupling performed according to manufacturer's recommendations) topolyclonal antibodies raised against E. coli O157:H7 (Bio-Trace affinitypurified; Kirkegaard & Perry Laboratories, Gaithersburg, Md., Cat. No.01-95-90). Anti-E. coli O157:H7 magnetic particles (1×10⁸/10 μl) weresonicated (1 min; setting 8; Fisher Scientific 550 Sonic Dismembrator).The magnetic beads are then added to blood (1 ml; Biochemed; Humanblood, sodium citrate as anticoagulant, cat. no. 10762WB) spiked with E.Coli O157:H7 (Strain DEC 3B, Dr. Tom Whittam, Pennsylvania StateUniversity). E. coli O157;H7 microcolony growth and detection areachieved follow the same steps used above in this example.

Example 13 Antimicrobial Susceptibility Testing Using In SituReplication and Non-Magnified Large Area Imaging

Background and objectives: The significance of antimicrobialsusceptibility testing for determining appropriate therapy is discussedin the background section. Monitoring microbial growth on solid mediumis common and has some significant advantages over growth in liquidculture. It is possible to inexpensively, simultaneously, andquantitatively determine the susceptibility of a strain of bacteria toseveral antibiotics without the use of instrumentation (e.g., using diskdiffusion assays), but the current methods require a purified colony andthus cannot usually be performed for 1-2 days after the patient's samplehas been processed. Such delays can be life threatening. Furthermore,another 1-2 days is generally required to detect and analyze the resultof a antimicrobial susceptibility test.

Objective. The example demonstrates the use of the invention todetermine the antibiotic susceptibility of bacterial strains rapidly.The principle of the method is diagrammed in FIG. 19. In the experimentdescribed below, resistant and sensitive strains were grown on mediawith and without antibiotic and microcolonies were detected as in theprevious example. The approach offers the potential to shortensignificantly the prolonged growth steps (colony purification and growthin antibiotic) that currently can delay implementation of appropriateantimicrobial therapy.

Methods. A sensitive (E. Coli MG1655) and resistant (E. coliMG1655/pLafr I) strain of bacteria were deposited on filters as in theprevious example (Example 1). Filters containing approximately 1000resistant bacteria were placed on LB plates (LB agar; Difco) that eithercontained antibiotic (tetracycline; 64 μg/ml) or did not containantibiotic. After incubation at 37° C. (3 hrs), the filters were stainedand imaged as in (Example 1).

Results. FIG. 21 shows the results of antimicrobial susceptibilitytesting using CCD-based non-magnified large area imaging. CCD imagingdetected microcolonies on the membrane containing resistant bacteria butnot on the membrane containing sensitive bacteria (compare the rowslabeled “resistant strain” and “sensitive strain” in the leftmost columnlabeled: 3 hour+tet, CCD). The intensity data obtained from imageanalysis quantified this observation (bar graph). High powerfluorescence microscopy confirmed that the resistant strain formedmicrocolonies after 3 hours of incubation, while the sensitive straindid not. (Microscopic analysis indicated that incubation of thesensitive strain in the presence of antibiotic leads to aberrantbacterial morphologies [compare the two microscopic images in the bottomrow labeled “sensitive strain”].).

The results of this experiment show that detecting microcolonies usingnon-magnified large area imaging is a rapid and sensitive method forantimicrobial susceptibility testing.

Variations: Some variations on the antimicrobial susceptibility testinclude using different signal moieties. Viability stains, such as Syto9 and other Syto family members (Molecular Probes), esterase substratessuch as fluorescein diacetate or chemchrome V6 (Chemunex), labeledantibodies, or metabolites that yield fluorescent products, could besubstituted for the nucleic acid stain in this assay. The naturalautofluorescence of the cellular target cells could also be used todetect the microcolonies. Microcolony growth could also be used tomonitor geometrical growth constraints as with antimicrobialsusceptibility testing disk diffusion or the E test methods (AB biodiskNA Inc.; E-test strips). The antimicrobial susceptibility assay can alsobe expanded to include simultaneous identification of various microbeswith different fluorescently labeled antibodies.

Example 14 Rapid Antimicrobial Susceptibility Testing Using the DiskDiffusion Method and Non-Magnified Large Area Imaging

Objective: This example demonstrates the use of the invention todetermine the antibiotic susceptibility of bacterial strains rapidlyusing the disk diffusion method. Disks that are impregnated with a knownconcentration of an antibiotic are placed on plates containing a largenumber of cells from a purified microbial culture. The antibioticdiffuses from the disk creating a radial gradient of antibioticconcentration centered on the disk (i.e., the closer to the disk, thehigher the concentration of antibiotic). Highly resistant strains cangrow in the presence of the disks even near the edge where theantibiotic concentration is highest. Less resistant strains grow outsideof a zone of inhibition surrounding the disk. The width of the zone ofinhibition is correlated with the level of antibiotic resistance for thestrain.

The zone of inhibition is traditionally measured by the naked eye afteran overnight growth. This example demonstrates the ability to determinethe zone of inhibition in hours by detecting the growth of microcoloniesusing non-magnified large area imaging.

Experimental Methods The strains, used in the example and described inExample 13, were diluted to 10⁶ CFU/ml and plated on TSA media. Atetracycline diffusion disk (Hardy Diagnostics; 30 μg tetracycline, cat.no. Z9121) was then placed on the plates. The plates were allowed toincubate at 37° C. for 5 hours. The microcolonies were imaged usingmicrocolony autofluorescence and non-magnified large area imaging as inprevious examples.

Results: FIG. 21 shows the results of a rapid antimicrobialsusceptibility test using non-magnified large area imaging. TheCCD-based imaging detected autofluorescent resistant colonies growingnear an antibiotic diffusion disk after only 5 hours. The zone ofinhibition was comparable to that obtained by visual inspection afterovernight growth. The results of this experiment show that detectingzones of inhibition based on microcolony growth is more rapid than thetraditional disk diffusion method but can yield comparable results.

Variations: This technique can be used with most antibiotic diffusiondisks and most microbes.

Example 15 Rapid Antimicrobial Susceptibility Testing Using the E-Test™and Non-Magnified Large Area Imaging

Objective: This example demonstrates the use of the invention to rapidlydetermine the antibiotic susceptibility of bacterial strains using anE-test™ antibiotic test strip. The E-test™ strip is impregnated with arange of concentrations of tetracycline enabling the user to use onestrip to determine the lowest antibiotic concentration needed to inhibitthe growth of the tested bacteria. This minimal inhibitory concentrationis based on the visualization of zones with no growth, called the zoneof inhibition. The zone of inhibition is traditionally measured by thenaked eye after an overnight growth. This example demonstrates theability to determine the zone of inhibition in hours by detecting thegrowth of microcolonies using non-magnified large area imaging.

Experimental Methods The strains, used in the example and described inExample 13, were diluted to 10⁶ CFU/ml and plated on TSA media. TheE-test™ strip (Hardy diagnostics: 0.016-256 μg tetracycline, cat. no.51002258) was then placed on the plates which were allowed to incubateat 37° C. for five hours. The microcolonies growing on or near the teststrip were imaged using microcolony autofluorescence and non-magnifiedlarge area imaging as in previous examples. After imaging, the plateswere allowed to incubate overnight.

Results: FIG. 22 shows the results of a rapid antimicrobialsusceptibility E-test™ using non-magnified large area imaging.Non-magnified, large area imaging detected autofluorescent resistantmicrocolonies growing near the E-test™ antibiotic test strip. A zone ofinhibition comparable with that observed after overnight growth could bedetermined after five hours of growth. The results of this experimentshow that detecting microcolonies using non-magnified large area imagingis a rapid and sensitive method greatly reducing the time to result foran E-test™.

Variations: This technique is applicable to E-Test™ strips impregnatedwith a variety of antibiotics.

Other Embodiments

All patents, patent applications, and publications referenced in thisapplication are hereby incorporated by reference. Other embodiments ofthe invention will be apparent to those skilled in the art fromconsideration of the specification and practice of the inventiondisclosed herein. It is intended that the specification and examples beconsidered as exemplary only, with a true scope and spirit of theinvention being indicated by the following claims. Examples of otherembodiments that may be adapted to the methods described herein arefound in U.S. application Ser. No. 10/237,010, entitled “RAPID ANDSENSITIVE DETECTION OF CELLS AND VIRUSES”, filed Sep. 6, 2002 and U.S.application Ser. No. 10/236,105, entitled “RAPID AND SENSITIVE DETECTIONOF MOLECULES”, filed Sep. 6, 2002, each of which is hereby incorporatedby reference.

Other embodiments are in the claims.

What is claimed is:
 1. An instrument for detecting microcolonies oftarget cells in a sample, said instrument comprising: (a) aphotoelectric array detector having associated optics to detect adetection area having at least one dimension that is ≧1 cm withoutmagnifying said detection area by more than 5-fold and an opticalresolution of less than 50 microns; (b) an illumination source thatilluminates said detection area; and (c) a computer programmed toreceive data collected by said photoelectric array detector, whereinsaid data is a digital representation of said detection area, andprogrammed for image analysis comprising analyzing said data to detectone or more microcolonies having a measurement of less than 50 micronsin at least two orthogonal dimensions, and programmed to quantify thenumber of microcolonies detected in said detection area, wherein saidinstrument detects a property of said one or more microcolonies thatdoes not depend on the addition of a signaling moiety or categorybinding molecule, and wherein said cells in said one or moremicrocolonies remain competent to replicate following detection.
 2. Theinstrument of claim 1, further comprising an incubator for microbialreplication.
 3. The instrument of claim 2, wherein said computer isprogrammed to detect said detection area at multiple time points duringincubation, wherein said sample is stored in said incubator between saidtime points, and movement of said sample between said incubator and saiddetector is automated.
 4. The instrument of claim 1, further comprisingan automatic focus for focusing on said detection area.
 5. Theinstrument of claim 1, wherein said instrument does not comprise animage intensifier.
 6. The instrument of claim 1, wherein said instrumentdetects living cells at a density of less than 100 target cells per mm²of the detection area, wherein within said detection zone said cells arerandomly dispersed and immobilized.
 7. The instrument of claim 1,wherein said associated optics do not magnify.
 8. The instrument ofclaim 1, wherein said one or more microcolonies have a measurement ofless than 10 microns in the longest linear dimension.
 9. The instrumentof claim 1, wherein said target cells are bacteria.
 10. The instrumentof claim 1, wherein said sample comprises an essentially planar solid orsemi-solid growth medium.
 11. The instrument of claim 1, wherein saidimage analysis further comprises detecting growing microcolonies. 12.The instrument of claim 1, wherein said instrument automatically loadssaid sample.
 13. The instrument of claim 1, wherein said detectingdetects light emitted, scattered, reflected, or absorbed as a result ofillumination of said one or more microcolonies.
 14. The instrument ofclaim 1, wherein said detecting detects autofluorescence emitted by saidmicrocolonies.
 15. The instrument of claim 1, wherein said illuminationsource employs one or more lasers.
 16. The instrument of claim 1,wherein said illumination source employs one or more light-emittingdiodes.
 17. The instrument of claim 1, further comprising one or moreoptical filters that only pass selected wavelengths of light.
 18. Theinstrument of claim 1, wherein said instrument automatically tracks saidsample by a bar code or equivalent label.
 19. The instrument of claim 1,wherein said instrument is programmed to align multiple images of thedetection area via registration marks.
 20. The instrument of claim 1,wherein said photoelectric array detector comprises a CCD detector,photomultiplier tube detector, or a photodiode detector.
 21. Theinstrument of claim 20, wherein said CCD detector is cooled.
 22. Theinstrument of claim 1, wherein said computer is programmed to determinethe locations in the detection area of said one or more microcolonies.23. The instrument of claim 22, wherein said computer is programmed tocompare said locations in the detection area of individual microcoloniesto previously determined locations of the same microcolonies.
 24. Theinstrument of claim 1, wherein said image analysis discerns objects thatchange size over time from objects that do not change size over time.25. The instrument of claim 1, further comprising an automated X-Y stagefor positioning said sample relative to said illumination source anddetector.
 26. The instrument of claim 1, wherein said computerautomatically saves output data for querying.
 27. An instrument fordetecting microcolonies of target cells, said instrument comprising: (a)a photoelectric array detector having associated optics to detect adetection area having at least one dimension that is ≧1 cm withoutmagnifying the detection area by more than 5-fold and an opticalresolution of less than 50 microns and encircle or ensquared energyvalues of greater than 50% per pixel; (b) an illumination sourceconfigured to illuminate said detection area having at least onedimension that is ≧1 cm; (c) a computer programmed to receive datacollected by said photoelectric array detector, wherein said data is adigital representation of said detection area, programmed for imageanalysis comprising analyzing said data to detect one or moremicrocolonies having a measurement of less than 50 microns in at leasttwo orthogonal dimensions, and programmed to quantify the number ofmicrocolonies detected in said detection area; (d) an incubator formicrobial replication; (e) an automated X-Y stage for positioning asample relative to said illumination source and detector; and (f) anautomatic focus for focusing on a detection area in said sample; whereinsaid instrument detects a property of said one or more microcoloniesthat does not depend on the addition of a signaling moiety or categorybinding molecule, and wherein said cells in said one or moremicrocolonies remain competent to replicate following detection.
 28. Theinstrument of claim 1, wherein the computer is programmed to detect saidmicrocolonies by an object-finding utility that joins contiguous pixelsthat have a value above an automatic or user-defined threshold toestablish a contour line around the perimeter of the object, wherein theperimeter pixels and those inside are defined as the object.
 29. Theinstrument of claim 27, wherein the computer is programmed to detectsaid microcolonies by an object-finding utility that joins contiguouspixels that have a value above an automatic or user-defined threshold toestablish a contour line around the perimeter of the object, wherein theperimeter pixels and those inside are defined as the object.
 30. Theinstrument of claim 1, wherein the associated optics comprise acollection lens and a focusing lens.
 31. The instrument of claim 27,wherein the associated optics comprise a collection lens and a focusinglens.